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This comprehensive survey analyzes over 200 recent papers on YOLO (You Only Look Once), a prominent real-time object detection framework, focusing on the evolution of its architectures and motivations for enhancements. The study categorizes various applications across diverse fields such as medical imaging, agriculture, and security, while providing a detailed comparison of the advantages and limitations of different YOLO versions. Key findings reveal that innovations like lightweight backbones, attention mechanisms, and improved loss functions significantly enhance detection accuracy and efficiency in complex scenarios, particularly for small and overlapping objects.
Innovations in YOLO architectures have led to substantial improvements in detection accuracy and efficiency, especially for challenging object detection tasks.
YOLO (You Only Look Once) is a popular real-time object detection method that has been widely used in most computer vision applications recently. YOLO is a fast and lightweight deep neural network that can provide the output in a single-stage operation. This paper is a comprehensive survey of more than 200 papers that have been published over the last two years about YOLO applications. This study explains the applications of YOLO and their motivations to change the architecture to enhance their operations. In this research all of the comparison criteria of different YOLO versions have been compared and the YOLO components have been revised comprehensively. The advantages and limitations of each YOLO versions have been discussed and the statistical comparisons of different YOLO versions in different applications have been illustrated. These applications include industrial, medical, agriculture, agronomy, transportation, security, monitoring, remote sensing, and animal-related. These applications are related to the classification, segmentation, detection, recognition, and localization of different objects in the grayscale, colored, X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Infrared (IR), Thermal, and synthetic aperture radar (SAR) images. For all of the revised papers the motivation, the improvement and the specifications of that application have been discussed and for some of these applications the results have been shown in some figures. In terms of the motivations of each reviewed paper, some researches replaced the backbone of the original YOLO with a lightweight network to reduce the parameters, FLOPs (floating point operations), and model size. Some of them, proposed an attention mechanism to enhance feature extraction capabilities of backbone. Diffusion mechanism of features and dimension integration of the neck are other innovations in most of the YOLO-based approaches to optimize the detection of small and overlapped objects. Furthermore, some of these researchers improved the loss function and head enhancement of YOLO to boost detection accuracy.