关键词: 目标检测, 缺陷检测, 注意力机制, 卷积神经网络, 深度学习, CenterNet

Abstract: In bamboo strip surface defect detection, the bamboo strip defects have different shapes and messy imaging environment, and the existing target detection model based on Convolutional Neural Network (CNN) does not take advantage of the neural network when facing such specific data; moreover, the sources of bamboo strips are complicated and there exist other limited conditions, so that it is impossible to collect all types of data, resulting in a small amount of bamboo strip defect data that CNN cannot fully learn. To address these problems, a special detection network aiming at bamboo strip defects was proposed. The basic framework of the proposed network is CenterNet. In order to improve the detection performance of CenterNet in less bamboo strip defect data, an auxiliary detection module based on training from scratch was designed:when the network started training, the CenterNet part that uses the pre-training model was frozen, and the auxiliary detection module was trained from scratch according to the defect characteristics of the bamboo strips; when the loss of the auxiliary detection module stabilized, the module was intergrated with the pre-trained main part by a connection method of attention mechanism. The proposed detection network was trained and tested on the same training sets with CenterNet and YOLO v3 which is currently commonly used in industrial detection. Experimental results show that on the bamboo strip defect detection dataset, the mean Average Precision (mAP) of the proposed method is 16.45 and 9.96 percentage points higher than those of YOLO v3 and CenterNet, respectively. The proposed method can effectively detect the different shaped defects of bamboo strips without increasing too much time consumption, and has a good effect in actual industrial applications.

Key words: object detection, defect detection, attention mechanism, Convolutional Neural Network (CNN), deep learning, CenterNet