基于YOLO的钢缆表面损坏检测

YOLO-based Surface Damage Detection of Steel Cables

为了解决检测钢缆表面损坏时检测设备资源有限、时间过长等问题,将深度学习的先进技术以及卷积神经网络(CNN)应用于钢缆表面损坏检测.提出了一种基于YOLO的缺陷检测网络模型,将GhostNet融入主干网络,并基于ShuffleNet及注意力机制提出了新的特征提取模块(ShuffleC3),再对Head部分进行剪枝改进.实验结果表明,改进后网络相比基线YOLOv5s的平均精度提高1.1%,参数量和计算量分别降低了43.4%和31%,模型大小减少了42.3%.可以在降低网络计算成本的同时,保持较高的识别精确度,更好地满足了对钢缆材料表面损坏检测的要求.

To solve the limited resources and long time of detection equipment in detecting surface damage of steel cables,this study applies advanced technology of deep learning and convolutional neural networks(CNNs)to surface damage detection of the cables.On this basis,it proposes a YOLO-based defect detection network model to integrate GhostNet into the backbone network,and a new feature extraction module(ShuffleC3)based on ShuffleNet and attention mechanism,and then prunes and improves the Head part.Experimental results show that compared with the baseline YOLOv5s,the average accuracy of the improved network is increased by 1.1%.In addition,the number of parameters and calculations are reduced by 43.4%and 31%respectively,and the model size is reduced by 42.3%.Thus,the proposed model can reduce the network computing cost and maintain higher identification accuracy,which better meets the requirements for surface damage detection of steel cable materials.