YOLO-LCD: A Lightweight Algorithm for Crosswalk Detection Based on Improved YOLOv5s

Intelligent recognition and location of crosswalks is a crucial element of autonomous driving. Achieving accurate real-time crosswalk detection with limited computational resources and multiple interference scenes has been troubling researchers. To address these issues, a lightweight convolutional neural network based on YOLOv5s, namely YOLO-LCD, is proposed. First, a lightweight network incorporating a coordinate attention(CA) mechanism is proposed as a replacement for the backbone network of YOLOv5s, which leads to a significant reduction in the parameter count of YOLOv5s by using depthwise convolution operations. Secondly, for better extraction of crosswalk features, Reparameterized Generalized-FPN(RepGFPN) is adopted as the neck of YOLO-LCD. Finally, for better localization of crosswalks, we use a dynamic label assignment strategy to better define positive anchors and use the EIoU loss rather than the CIoU loss. Additionally, the detection algorithm that we propose is verified on the crosswalk dataset containing complex scenarios. The experimental results demonstrate that the YOLO-LCD algorithm obtains a surprisingly higher F1 score of 97.8% with 80.2% fewer parameters and 82.9% fewer FLOPs than YOLOv5s, and the FPS on an old CPU device is 186.4% faster. This study provides a solution for accurate real-time crosswalk detection under limited computing resources.