Lightweight Network Model for Waste Cable Detection
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 5.15×10 6 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.
万方数据
维普
