Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.

Hao Chen

Zhan Chen

Hang Yu

Sensors

SENSORS (ISSN 1424-8220) 为传感器和生物传感器的科学和技术提供了一个高级论坛。它发表评论（包括对完整传感器产品的综合评论）、定期研究论文和简报。我们的目标是鼓励科学家尽可能详细地发表他们的实验和理论结果。论文的长度没有限制。必须提供完整的实验细节，以便重现结果。

"Sensors" (ISSN 1424-8220) is a high-level forum dedicated to the science and technology of sensors and biosensors. The journal publishes various types of articles, including comprehensive reviews of complete sensor products, regular research papers, and brief communications. Our aim is to encourage scientists to share their experimental and theoretical results in as much detail as possible. There is no limit on the length of the papers, but complete experimental details must be provided to allow for the reproducibility of results.

《Sensors》（ISSN 1424-8220）是一个专注于传感器和生物传感器科学与技术的高级论坛。期刊接受各种类型的文章，包括对完整传感器产品的综合评论、定期研究论文和简报。我们的目标是鼓励科学家尽可能详细地分享他们的实验和理论结果。论文的长度没有限制，要求提供完整的实验细节，以便他人能够重现结果。

Enhanced YOLOv5: An Efficient Road Object Detection Method

The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes.

Enhanced YOLOv5: An Efficient Road Object Detection Method

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