基于改进CenterNet的竹条表面缺陷检测方法

在竹条表面缺陷检测中，竹条表面缺陷形状各异，成像环境脏乱，现有基于卷积神经网络（CNN）的目标检测模型面对这样特定的数据时并不能很好地发挥神经网络的优势；而且竹条来源复杂且有其他条件限制，因此没办法采集所有类型的数据，导致竹条表面缺陷数据量少到CNN不能充分学习。针对这些问题，提出一种专门针对竹条表面缺陷的检测网络。该网络的基础框架为CenterNet，而且为提高CenterNet在较少的竹条表面缺陷数据中的检测性能，设计了一种基于从零开始训练的辅助检测模块：在网络开始训练时，冻结采用预训练模型的CenterNet部分，并针对竹条的缺陷特点从零开始训练辅助检测模块；待辅助检测模块损失趋于稳定时，通过一种注意力机制的连接方式将该模块与采用预训练的主干部分进行融合。将所提检测网络与CenterNet以及目前常用于工业检测的YOLO v3在相同训练测试集上进行训练和测试。实验结果表明，所提检测网络的平均精度均值（mAP）在竹条表面缺陷检测数据集上比YOLO v3和CenterNet的mAP分别提高了16.45和9.96个百分点。所提方法能够针对形状各异的竹条表面缺陷进行有效检测，且没有增加过多的时耗，在实际工业运用中具有很好的效果。

Bamboo strip surface defect detection method based on improved CenterNet

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.