基于S-YOLOV3模型的织物实时缺陷检测算法

Real-time Fabric Defect Detection Algorithm Based on S-YOLOV3 Model

为了满足工业上对织物缺陷检测的实时性要求,提出一种基于S-YOLOV3(Slimming You Only Look Once Version 3)模型的织物实时缺陷检测算法。首先使用K均值聚类算法确定目标先验框,以适应不同尺寸的缺陷;然后预训练YOLOV3模型得到权重参数,利用批归一化层中的缩放因子γ评估每个卷积核的权重,将权重值低于阈值的卷积核进行剪枝以得到S-YOLOV3模型,实现模型压缩和加速;最后对剪枝后的网络进行微调以提高模型检测的准确率。实验结果表明:对于不同复杂纹理的织物,所提模型都能准确检测,且平均精度均值达到94%,剪枝后检测速度提高到55 FPS,所得的准确率与实时性均满足工业上的实际需求。

To meet the real-time requirements of fabric defect detection in industry,a real-time fabric defect detection algorithm based on S-YOLOV3(Slimming You Only Look Once Version 3)model is proposed.To develop this algorithm,the K-means clustering algorithm is used to determine the target prior frame for adapting to different sizes of defects.The YOLOV3 model is then pretrained to obtain the weight parameters,and the scaling factorγis used in the batch normalization layer to evaluate the weight of each convolution kernel.The convolution kernel with weight value lower than the threshold is clipped to obtain the S-YOLOV3 model to achieve compression and acceleration.Finally,the pruned network is fine-tuned to improve the model detection accuracy.Experiment results reveal that the proposed model provides accurate detection of fabrics with different complex textures(average precision of 94%).After pruning,the detection speed is increased to 55 FPS.The obtained accuracy and real-time can meet the actual demand of industry.