Road foreground small object detection algorithm based on improved YOLOv5s

Detection of small objects is fundamental for advanced driving assistance systems (ADAS) to implement avoidance measures. Nevertheless, detecting small objects on roads presents a significant challenge. To tackle this challenge, this paper proposes an enhanced algorithm for detecting road foreground small objects using YOLOv5s. First, the Polarized Self-Attention (PSA) module is introduced to boost the performance of the YOLOv5s model. This module allows the model to prioritize foreground object learning while reducing attention to the background, thus improving detection robustness in complex background conditions. Second, optimization of the loss function is conducted by utilizing the Area-Based IoU (ABIoU) Loss, which imposes constraints on object regression loss based on area ratios. This significantly improves the model's ability to learn image features and enhances the accuracy of object detection. The experimental findings reveal that the enhanced YOLOv5s model demonstrated a notable improvement over the original model, with an increase in mAP of 2.7% and 2.9% on the Lost and Found and MS COCO datasets, respectively. Additionally, the precision witnessed a corresponding enhancement of 3.5% and 3.2%. This advancement facilitates the more efficient and precise detection of road foreground small objects.