# Road Obstacle Detection Method Based on Improved YOLOv5
###### Abstract
YOLOv3 further refines the model by replacing the backbone network from Darknet19 to Darknet53 and using logistic regression instead of softmax, achieving a better balance between real-time performance and accuracy. YOLOv4 tests various commonly used techniques and incorporates them into the traditional YOLO architecture to optimize the balance of speed and accuracy. Overall, with the continuous advancement of deep learning technology, target detection algorithms have made significant strides in both accuracy and efficiency. While two-stage and single-stage methods each have their own advantages and limitations, their combination and further improvement provide strong support for practical applications, such as road obstacle detection. Obstacle detection has become a crucial technology in various fields where unidentified hazards can jeopardize operational safety. [PERSON] et al. [10] emphasized the importance of obstacle detection in radiation therapy rooms, as obstacles can disrupt the treatment process. They developed a software solution based on the YOLOv5 object detection algorithm, offering detection accuracy similar to manual methods, which enhances safety in radiation therapy and enables intelligent detection. In urban road detection, [PERSON] et al. [11] proposed a lightweight architecture that achieves high mAP in complex traffic scenarios, while [PERSON] et al. improved an algorithm for the real-time detection of outdoor road damage is proposed to promote a safe and cost-effective transportation network [12]. These modifications effectively reduced the model's memory footprint, leading to improved performance and efficiency. In another approach, [PERSON] et al. [13] introduced a two-stage road obstacle detection framework combining point cloud denoising with density clustering and an optimized RPN, boosting detection robustness and processing speed. [PERSON] et al. [14] optimized YOLOX by incorporating lightweight attention modules, achieving a good balance between speed and accuracy [15]. [PERSON] et al. [16] developed the SME-YOLO framework, a lightweight architecture enhancing detection reliability in blind alley scenarios with computational constraints [17]. [PERSON] et al. [18] tackled challenges in autonomous driving with a pixel-level segmentation network using multi-scale feature fusion and adaptive context modeling, which significantly improved small obstacle recognition. [PERSON] focused on improving detection technology during vehicle operations, reducing personnel fatigue and bias during inspections [19]. In drone scenarios, the ability to detect obstacles during flight is a fundamental requirement for widespread usage [20]. [PERSON] et al. [21] enhanced UAV navigation through spatial attention fusion, enabling precise obstacle mapping. [PERSON] et al. [22] proposed a lightweight real-time detection method by modifying the backbone network, integrating an attention mechanism, and incorporating a residual structure. In the context of industrial safety, [PERSON] et al. [23] developed an integrated framework for hazardous environment robots, particularly in tunneling scenarios, to facilitate real-time obstacle avoidance for heavy machinery. Similarly, dynamic obstacle detection systems have been developed for rail-road infrastructure safety [24; 25; 26]. [PERSON] et al. [27] advanced agricultural robotics by proposing a partial convolution-based network combined with coordinate attention (CA) mechanisms within the YOLOv5 framework to improve detection accuracy. [PERSON] et al. [28] further enhanced obstacle detection in orchards using a binocular vision-enhanced YOLOv4 system, achieving real-time positioning through optimized hardware deployment [29]. [PERSON] et al. [30] proposed the Feffol neural architecture for ensuring safe navigation of unmanned vehicles in open-pit mining environments, ensuring reliable operation for autonomous mining vehicles [31].