Improved Tiny-YOLOv3 Defect Detection Algorithm for Industrial Steel
The deep learning network model has a large number of parameters and is not suitable for embedded or mobile devices.Aiming at the problem of real-time detection in industrial steel production,an improved R-Tiny-YOLOV3defect detection algo-rithm for industrial steel was proposed.Firstly,the residual network structure is added into the Tiny-YOLOv3 structure to im-prove the detection accuracy.The space pyramid SPP network module is added to improve the ability of network feature extraction.Combined with the characteristic information of different network layers,the detection can be improved to three scales.Then,CIOU is selected as the loss function to make the regression of the target detection box more stable.Finally,the data set was en-hanced and tested on Cambricon 1H8embedded platform.The experimental results show that the improved R-Tiny-YOLOV3al-gorithm can detect defect targets in real time,the average accuracy is increased by 10.8%,and the operation speed can reach 39.8 frames/s,which provides a reference for embedded application of industrial steel defect detection.
NETL
NSTL
万方数据
维普
