Target detection system for surface cracks of hot continuous steel casting based on YOLO V4 model

Renbo Zhang; Yuzhou Cai; Huilin Liu

doi:10.1117/12.2635816

6 May 2022 Target detection system for surface cracks of hot continuous steel casting based on YOLO V4 model

Renbo Zhang, Yuzhou Cai, Huilin Liu

Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 1225639 (2022) https://doi.org/10.1117/12.2635816
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China

Abstract

In the process of continuous casting slab production, serious defects will have an adverse impact on the subsequent rolling process. The target detection of cracks using machine vision algorithm has been increasingly applied in industry. The detection of defects in hot billets is of great significance. Adjusting the flow and flow rate of mould in advance can prevent more defective billets from being produced. In this paper, the detection system of hot billet is constructed by combining YOLO (You Only Look Once) and public data set, which can realize the defect detection in industrial production. Combined with the two algorithms of YOLO V3 and YOLO V4, the system detection results are compared, and a comparative conclusion is drawn. YOLO V4 algorithm uses multi-scale detail boosting at the input for image enhancement, and the part of neck adopts SPP module and FPN + PAN mode, and on this premise, the definition of partial loss function is changed. These changes eventually make YOLO V4 faster, more accurate and lighter. According to the experimental results of this paper, the following conclusions are drawn: This system can realize the defect detection of hot continuous steel casting in industry. The maximum value has not been greater than 0.1, while GIoU is lower than 0.02 when the epcho is greater than 200. The accuracy of YOLO V4 training prediction framework is much higher than that of YOLO V3, and the target detection is also more accurate. In terms of recall rate and average AP value of various categories, YOLO V4 is better, with a maximum increase of 0.1. At the same time, among the samples divided into positive examples into all crack categories, the average proportion of actual positive examples is also higher.

Citation Download Citation

Renbo Zhang, Yuzhou Cai, and Huilin Liu "Target detection system for surface cracks of hot continuous steel casting based on YOLO V4 model", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 1225639 (6 May 2022); https://doi.org/10.1117/12.2635816

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KEYWORDS

Image enhancement

Target detection

Image fusion

Defect detection

Neck

Inspection

Detection and tracking algorithms

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