Detecting small objects with yolo Aug 8, 2025 · Learn how to implement YOLO11 with SAHI for sliced inference. It is commonly implemented using OpenCV for image/video processing and YOLO (You Only Look Once) models for real-time detection. Nov 16, 2025 · Object Detection Object detection is a task that involves identifying the location and class of objects in an image or video stream. Therefore, we propose a universal structure for all There are always trade-offs between accuracy and speed but this is a nice approach and framework to detect very small objects which can't be done with just a single model pass. Apr 10, 2025 · Additionally, a small-target detection layer is added to enhance the model’s ability to perceive small targets. Jul 15, 2023 · The most significant technical challenges of current aerial image object-detection tasks are the extremely low accuracy for detecting small objects that are densely distributed within a scene and the lack of semantic information. Jun 19, 2025 · In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. Jan 24, 2025 · Object detection is crucial for remote sensing image processing, yet the detection of small objects remains highly challenging due to factors such as image noise and cluttered backgrounds. Small object detection is usually a challenging task since the size of the objects makes it difficult for the features to be adequately represented in the backbone. Section 3, introduces the proposed MSF-YOLO, detailing specific modifications and improvements to address challenges in small object detection tasks, including multi-scale feature fusion and clustering optimization of initial anchor frames. 1%. The main contributions of this paper can be summarised as follows: I. In this paper, we propose LGFF-YOLO, a detection model that integrates a novel local–global feature fusion method with the YOLOv8 baseline, specifically This paper introduces YOLO v8_CAT, an advanced object detection model designed to improve the accuracy of small and challenging object detection in traffic light recognition tasks. Our approach Mar 1, 2025 · While many high-accuracy deep learning solutions have been developed, their large parameter sizes hinder deployment on edge devices where low latency and efficient resource use are essential. 8) or SSD513 (10. Tiling your images is a valid approach to maintain resolution and potentially improve recall. In underwater object detection, a crucial method for marine exploration, the presence of small objects in underwater environments significantly hampers the performance of detection. but am not able to detect small objects. Mar 30, 2025 · In the perception of unmanned systems, small object detection faces numerous challenges, including small size, low resolution, dense distribution, and occlusion, leading to suboptimal perception performance. com/ai-vision-academyLearn how to dramatically improve small object detection accuracy using YOLO and t You Only Look Once (YOLO)[15], a one-stage object detection algorithm, is dominating UAV systems due to its low latency and high accuracy. The generalizability of the CPDD-YOLOv8 model is verified on the WiderPerson, VOC_MASK and SHWD datasets. Jan 2, 2022 · This was because of how inaccurate YOLO was at detecting small objects. That’s why I’m excited to share my latest project: YOLO Slicing for segmentation. This research contributes to the ongoing development of object detection methodologies and establishes a robust foundation for future work in small object detection. pt) Part of the COCO2017 dataset and the SODA-D dataset were Jul 2, 2025 · The LRDS-YOLO model proposed in this paper demonstrates exceptional performance in UAV small object detection tasks, particularly in terms of detection accuracy and computational efficiency. Feb 8, 2025 · In view of the complex environments and varying object scales in drone-captured imagery, a novel PARE-YOLO algorithm based on YOLOv8 for small object detection is proposed. txt) or read online for free. I learned that I needed a dataset, so I took a few screenshots. Moreover, existing Dec 24, 2024 · This paper proposes a lightweight object detection model, DC-YOLO, integrating dynamic convolution and YOLOv5s to address limited computational resources and small target detection challenges in UAV Oct 28, 2024 · Abstract Small object detection, which is frequently applied in defect detection, medical imaging, and security surveillance, often suffers from low accuracy due to limited feature information and blurred details. Apr 15, 2025 · This study proposes LSOD-YOLO, a lightweight small object detection algorithm based on YOLOv8, specifically designed to address these issues. To address these issues, this paper introduces an enhanced small About SOD-YOLO (Small Object Detection YOLO) builds upon the foundational YOLOv8 model to address the unique challenges of detecting small objects in complex backgrounds typical of UAV imagery. The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. maximum resolution for target observation in real-time applications. At a high level, it involves predicting the locations and Mar 3, 2025 · Object detection models are widely applied in the fields such as video surveillance and unmanned aerial vehicles to enable the identification and monitoring of various objects on a diversity of backgrounds. Remote sensing of small object detection plays an important role in areas such as environmental 👉 AI Vision sources + Community → https://www. To address these issues, this letter proposes an improved YOLOv8 algorithm—PA-YOLO [poly Kernel inception and contextual anchor attention (PC-C2f) and attention scale sequence fusion feature pyramid network and dysample upsampling Sep 6, 2024 · Images captured by Unmanned Aerial Vehicles (UAVs) play a significant role in many fields. Here are my questions: How many images required per class ? (am using 2 classes. Our approach Apr 14, 2025 · PDF | This paper provides an extensive evaluation of YOLO object detection models (v5, v8, v9, v10, v11) by com- paring their performance across various | Find, read and cite all the research Apr 7, 2025 · Unlike traditional object detection models that first propose regions and then classify them (like Faster R-CNN), YOLO directly predicts objects and their locations in a single neural network run. . Mar 5, 2025 · To address this issue, we propose L-YOLO, an improved lightweight road object detection algorithm based on YOLOv8s. Among them, the size of the detection feature map corresponding to P3/8 is 80x80, which is used to detect arXiv. For datasets like Visdron2021 and Tinyper-son, there are challenges related to small, dense, and numerous objects that conventional object detection models struggle to detect efectively. To address this, we propose LEAF-YOLO, a lightweight and efficient object detection algorithm with two versions: LEAF-YOLO (standard) and LEAF-YOLO-N (nano). This is the case May 2, 2025 · Hi, I’m using YOLOv8 and it’s missing small objects in my images. To address these issues, we propose SMN-YOLO, a lightweight small object detector based on YOLOv8n. In this paper, we proposed a multilayer feature fusion algorithm named SCA-YOLO (spatial and coordinate attention Jul 23, 2025 · In this article, we’ll explore how to implement object detection with YOLOv3 using TensorFlow. Mar 18, 2025 · Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited Jun 14, 2024 · Learn how to detect small objects using SAHI with the Inference Slicer Python method, and using various pre-processing steps. Jul 22, 2025 · Object detection in remote sensing imagery presents significant challenges due to scale variations and complex backgrounds, especially for small objects. This modernized approach allows YOLO models to detect objects with speed, accuracy, and efficiency. It is important to consider the specific requirements of your application when choosing an object detection algorithm. #Pyresearch #ComputerVision #OpenCV #SAHI #custom #datasets Learn how to enhance small object detection using SAHI (Slicing Aided Hyper Inference) with custom YOLO models. Sep 6, 2024 · Images captured by Unmanned Aerial Vehicles (UAVs) play a significant role in many fields. We would like to show you a description here but the site won’t allow us. Unlike larger objects, small objects contain limited spatial and contextual information, making accurate detection difficult. The YOLO (You Only Look Once) algorithm is a popular one-stage algorithm for small-scale object detection. The earlier versions of YOLO struggled with small object detection tasks. The code, pretrained models, and dataset processing scripts will be released upon acceptance of the Mar 10, 2022 · Since lack of shallow features extraction can cause small object miss-detection and lower the detection accuracy, PANnet is modified in SO-YOLO to reserve shallow feature information. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. Moreover, the extensive parameter count and computational demands of the detection models impede their deployment on Dec 6, 2023 · I am working on a task to detect small objects in a 416x1424 (H x W) image using the YOLOv8 model. In standard object detection tasks, when there are small objects in the data set, the problem of missing detection or poor detection effect often occurs. Apr 10, 2025 · YOLO’s strength lies in its ability to detect objects in real-time, but due to its single-pass approach, it sometimes still struggles with small objects or closely packed objects in an image. Dec 8, 2024 · The YOLO series detection models play a crucial role in target detection tasks. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects (that is, objects that occupy a small pixel area in the input image) a genuinely Detect small objects in full-resolution images using tiled training of a you only look once version X (YOLOX) deep learning network. In this paper, we propose YOLO-TLA, an advanced object detection model building on YOLOv5. Accurate detection of small objects such as pedestrians, vehicles, motorcycles, bicycles, traffic signs, and lights is crucial for safe navigation and Learn about the YOLO object detection architecture and real-time object detection algorithm and how to custom-train YOLOv9 models with Encord. In view of the insufficient detection ability of YOLOF (You Only Look One-level Feature) detector for small objects at low resolution, we propose You should try increasing the zoom and mosaic augmentation values and play around with other relevant augmentations for the training. We introduce Nov 1, 2025 · Detecting small objects in drone aerial imagery presents significant challenges, particularly when algorithms must operate in real-time under computational constraints. Dec 22, 2021 · As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. In the field of target detection, YOLO model is a popular real-time target detection algorithm model that is fast, efficient, and accurate. It is currently under review for publication. To address these issues, we propose a specialized algorithm named Unmanned-system Small-object Detection-You Only Look Once (USD-YOLO). yaml). YOLOv12. In terms of datasets, we build a large-scale dataset with high image resolution dubbed Small-PCB, in order to promote detection in Jul 29, 2024 · Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. Jan 1, 2023 · In real life, object detection is widely applied and plays a significant part in the field of computer vision. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. Any tips on how to improve detection for smaller items? Sep 11, 2025 · Object detection is a widely used task in computer vision that enables machines to not only recognize different objects in an image or video but also locate them with bounding boxes. For datasets like Visdron2021 and Tinyperson, there are challenges related to small, dense, and numerous Feb 15, 2025 · However, challenges such as low accuracy, missed detections, and occlusion still persist when detecting small objects from aerial perspectives. A small object detection algorithm based on Dec 1, 2024 · DS-YOLO was trained and tested on the CrowdHuman and VisDrone2019 datasets, which contain a large number of densely populated pedestrians, vehicles and other objects. To address this issue, we propose the SOD-YOLO model based on YOLOv7, which incorporates a DSDM-LFIM backbone network and includes a small object detection branch. In this paper, we propose LGFF-YOLO, a detection model that integrates a novel local–global feature fusion method with the YOLOv8 baseline, specifically Jul 23, 2025 · In this article, we’ll explore how to implement object detection with YOLOv3 using TensorFlow. Feb 22, 2024 · Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. This research aims to optimize the latest YOLOv8 model to improve its detection of small objects and compare it with another different version of YOLO models. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. Jun 19, 2025 · Understand what is YOLO for object detection, how it works, what are different YOLO models and learn how to use YOLO with Roboflow. Baseline Detection Small object detection in high-resolution images presents challenges due to the objects' size relative to the image resolution. A small object detection algorithm based on Nov 23, 2024 · Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. load(yolov8m. We first introduce an additional Apr 22, 2024 · How to Detect Small Objects Using Slicing Aided Hyper Inference Object detection is one of the fundamental tasks in computer vision. Feb 20, 2024 · Small object detection is a challenging task in computer vision. In addition, there is a need to balance performance and complexity when improving the model Jun 12, 2024 · I've just started learning about artificial intelligence image capture. Jan 1, 2024 · Having a tiny scale and few identifiable features, small objects are particularly difficult to detect, especially when the image resolution is not high. The proposed lightweight cross-layer output reconstruction (LCOR) module enhances small object detection by strengthening the integration of shallow and deep feature information through cross-layer Dec 17, 2024 · The identification of minuscule objects in remote sensing data presents a formidable challenge in computer vision, where objects may occupy a mere handful of pixels. Jan 3, 2025 · Unmanned aerial vehicle (UAV) image object detection has extensive applications across both civilian and military domains. Apr 23, 2025 · We’ll walk through real examples such as ant detection, vehicle tracking from drone views, and people detection in dense crowds. Specifically, a Oct 1, 2025 · While current object detection algorithms have shown strong performance in traffic sign detection, they still face difficulties with small object recognition, often resulting in missed or false detections. Moreover, the extensive parameter count and computational demands of the detection models impede their deployment Jun 3, 2024 · Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. The reason is stated as follows: The YOLOv8 model has 3 detection heads by default, which can perform multi-scale detection of targets. Mar 22, 2023 · Okay strap in- YOLO (You Only Look Once) is a popular set of object detection models used for real-time object detection and classification in computer vision. Since it assigns grid cells responsibility for objects based on their center points, smaller objects can get lost May 19, 2025 · Drone object detection serves as a fundamental task for more advanced applications. skool. However, with the development of UAV technology, challenges such as detecting small and dense objects against complex backgrounds have emerged. ) For smaller object Jan 16, 2025 · Discover how YOLO models excel in real-time object detection, from sports tracking to security. This addresses the issue of detail loss in deep Jan 4, 2025 · The CPDD-YOLOv8’s small object detection rate is improved by 13. Nov 20, 2024 · Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. That being said, I would say the best would be to train an instance segmentation model if possible using YOLOv8. 1 Introduction Detecting small objects in images can be challenging, mainly due to limited resolution and context information avail-able to a model [2]. Whether you’re working with a webcam, USB camera, or IP camera, integrating YOLO with your video feed can provide powerful object detection capabilities in real time. In this walkthrough, you’ll learn how to use a technique called SAHI (Slicing Aided Hyper Inference) in conjunction with state-of-the-art object detection models to improve the detection of small objects. For this guide, we will use a. In this paper, a dynamic YOLO detector is proposed as a solution to alleviate this problem. Feb 20, 2025 · YOLO12: Attention-Centric Object Detection Overview YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. To use SAHI with YOLOv12, we will: Let's get started! First, install the supervision pip package: Once you have installed supervision, you are ready to load your data and start writing logic to filter detections. Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. May 9, 2025 · To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. First, we designed an innovative module called the May 25, 2023 · Object detection from UAV (unmanned aerial vehicle) images is a crucial and challenging task in the field of computer vision. Nov 24, 2023 · You're correct that YOLOv8, by default, rescales images to a smaller size, which can impact the detection of small objects. Jul 17, 2025 · Abstract Small object detection remains a challenging problem in the field of object detection. SOD-YOLO is a YOLOv8-based object detection model designed to improve small object detection performance in complex aerial scenes, particularly those captured by UAVs. The Ultralyics library is a powerful toolkit for building state-of-the-art AI models, including YOLO (You Only Look Once Sep 1, 2025 · DSOD-YOLO is a lightweight small-object detection network based on YOLOv8, designed to balance detection accuracy with model efficiency. "Small objects" are objects having a small pixel footprint in the input image. Aug 8, 2024 · Object detection in computer vision plays a crucial role across various fields, including Autonomous Vehicles [1, 2, 3], traffic scene monitoring [4, 5], enhancing intelligent driving systems [6], and facilitating search and rescue missions [7]. It takes an image as input and outputs the information of the objects in one stage. org 提供數學、物理、計算機科學等領域的最新研究論文，為研究人員和學生提供一個開放的學術交流平台。 Feb 22, 2024 · This research aims to optimize the latest YOLOv8 model to improve its detection of small objects and compare it with another different version of YOLO models. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of Jun 25, 2025 · These findings confirm that targeted customization of YOLO’s architecture can effectively address the challenges associated with small object detection. Most deep learning-based detectors do not exploit the Jan 6, 2024 · Section 2 comprehensively reviews the development of target detection and small object detection. Optimize memory usage and enhance detection accuracy for large-scale applications. In areas such as aerial imagery, state-of-the-art object detection techniques under performed because of small objects. Conclusion In this tutorial, we walked through how to set up a real-time object detection system using YOLOv5, Python, and OpenCV. By fundamentally reducing information loss and incorporating a cross-scale feature fusion Aug 10, 2023 · Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. The diminutive size of these objects, coupled with the complex backgrounds in Oct 1, 2025 · To tackle these problems, a lightweight small object detection model is proposed in this article, which is called multi-stage feature enhancement lightweight-YOLO (MFEL-YOLO). These algorithms use a single neural network to simultaneously generate region proposals and perform object classification. By the end, you’ll know how to significantly improve detection results using intelligent slicing and, when needed, custom model training. Jul 29, 2024 · Abstract Object detection, a crucial aspect of computer vision, has seen significant advancements in accuracy and robustness. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. To address these challenges, we propose SOD-YOLO, an innovative model based on the YOLOv8 model, to detect small objects in UAV images. We claim that the huge performance gap between the small object detectors and normal sized object detectors stems from two aspects, including the small object dataset and the small object itself. Jun 25, 2025 · The results demonstrate that SO-YOLOv8 effectively identifies small objects within PASCAL VOC, overcoming one of the key limitations of traditional YOLO models and validating its adaptability to datasets where small objects are more dif