Journal Article

YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices

Wu, Chenguang, Zhang, Jiale, Ma, Yuchuan, Ye, Min

2023

Overview

To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices. First, a novel lightweight module, the LWC, is designed and the attention mechanism and activation function are optimized. Then, a lightweight backbone network and an efficient feature fusion network are further proposed with the LWC as the basic building units. Finally, the backbone and feature fusion network in the YOLOv5 is replaced. In this paper, two versions of the YOLO-LWNet, small and tiny, are introduced. The YOLO-LWNet was compared with the YOLOv6 and the YOLOv5 on the RDD-2020 public dataset in various performance aspects. The experimental results show that the YOLO-LWNet outperforms state-of-the-art real-time detectors in terms of balancing detection accuracy, model scale, and computational complexity in the road damage object detection task. It can better achieve the lightweight and accuracy requirements for object detection for mobile terminal devices.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / attention mechanism / Datasets / Deep learning / Detectors / Lasers / lightweight network / Machine learning / Methods / mobile terminal / Neural networks / object detection / road damage detection / Telecommunication systems / Traffic flow / YOLOv5