National Engineering Research Center of Highway Maintenance Equipment, Chang'an University, Xi'an 710065, China.
Sensors (Basel). 2023 Mar 20;23(6):3268. doi: 10.3390/s23063268.
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.
为了解决移动终端设备资源受限条件下的道路损坏目标检测需求，本文提出了一种高效的轻量级移动终端设备道路损坏检测算法 YOLO-LWNet。首先，设计了一种新颖的轻量级模块 LWC，并对注意力机制和激活函数进行了优化。然后，进一步提出了轻量级骨干网络和高效特征融合网络，以 LWC 作为基本构建单元。最后，替换了 YOLOv5 中的骨干网络和特征融合网络。本文引入了 YOLO-LWNet 的两个版本，即小型和微型。在 RDD-2020 公共数据集上，将 YOLO-LWNet 与 YOLOv6 和 YOLOv5 进行了多方面的性能比较。实验结果表明，在道路损坏目标检测任务中，YOLO-LWNet 在平衡检测精度、模型规模和计算复杂度方面优于最先进的实时检测器。它可以更好地满足移动终端设备对目标检测的轻量级和准确性要求。
