Lightweight Network Model for Waste Cable Detection

doi:10.19678/j.issn.1000-3428.0068537

Abstract

Abstract:

Currently, waste cable recycling in China relies primarily on manual sorting, which is time-consuming, labor-intensive, and inaccurate. To deploy the model on small embedded devices more effectively and improve real-time detection, an improved lightweight network model based on YOLOv5s for waste cable detection is proposed herein. First, standard convolution modules in the Backbone of the network are replaced with lightweight Ghost modules to reduce network complexity, and a Convolutional Block Attention Module (CBAM) is introduced before a Fast Spatial Pyramid Pooling (SPPF) module to enhance feature extraction and fusion efficiency. Second, the C3 module in the neck of the network is combined with an Effective Channel Attention (ECA) module to facilitate inter-channel information interaction and enhance the network's feature fusion capability. Finally, Wise Intersection over Union (WIoU) is utilized as a new bounding box loss function to improve the regression effect and accelerate the model convergence speed. The experimental results demonstrate that the improved model achieves an average detection accuracy of 96.3%, which is 1.2 percentage points higher than that of the Single Shot multibox Detector (SSD). There are 5.15×10⁶ parameters in the proposed model, which is a 27.0% reduction compared with the YOLOv5s model. The inference speed on the small embedded device, LubanCat-1, reaches 8.49 frame/s, indicating excellent real-time performance and suitability for real-time detection and classification of waste cables.

Key words: waste cable detection, YOLOv5s model, lightweight, attention mechanism, embedded device

摘要：

目前我国废旧电缆回收主要依靠人工分拣, 存在耗时耗力、准确性低的问题。为了更好地将模型部署至小型化硬件设备并提高检测实时性, 提出基于改进YOLOv5s的废旧电缆检测网络模型。首先, 将主干网络中的标准卷积模块替换为轻量化的幻象(Ghost)模块以减小网络的复杂度, 并且在快速空间金字塔池化(SPPF)模块前引入卷积块注意力模块(CBAM), 提高了特征提取和融合的效率。其次, 将网络中Neck部分的C3模块结合有效通道注意力模块(ECA), 实现了跨通道的信息交互, 提高了网络特征融合能力。最后, 在损失函数的计算部分使用Wise-交并比(WIoU)作为新的边界框损失函数以提升回归效果, 加快模型的收敛速度。实验结果表明: 改进模型的平均检测精度为96.3%, 相比单点多框检测器(SSD)提高了1.2个百分点; 参数量为5.15×10⁶, 相比YOLOv5s模型减少了27.0%;在小型嵌入式设备LubanCat-1上的推理速度达到8.49帧/s, 具有良好的实时性, 适用于废旧电缆的实时检测与分类。

关键词: 废旧电缆检测, YOLOv5s模型, 轻量化, 注意力机制, 嵌入式设备

Rixin RAO, Yiwen WANG, Lizhi ZENG, Xintian TONG, Haitao ZHAO. Lightweight Network Model for Waste Cable Detection[J]. Computer Engineering, 2024, 50(8): 22-30.

饶日昕, 王怡文, 曾砺志, 童心恬, 赵海涛. 面向废旧电缆检测的轻量化网络模型[J]. 计算机工程, 2024, 50(8): 22-30.

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Figures/Tables 15

Fig.1 Structure of improved YOLOv5s network

Fig.2 Structure of Ghost module

Fig.3 Structure of GhostBottleneck and C3Ghost

Fig.4 Structure of CBAM module

Fig.5 Structure of ECA module

Fig.6 Structure of C3ECA module

Fig.7 Classification of waste cable dataset

Fig.8 Data augmentation processing

Fig.9 Visual comparison of detection results

Fig.10 Comparative experimental results of different runtime environments

Fig.11 Comparison of loss curves

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