An Appearance Defect Detection Method for Cigarettes Based on C-CenterNet

TL;DR

This paper introduces C-CenterNet, a novel cigarette defect detection method that combines advanced neural network techniques to improve accuracy and adaptability in industrial cigarette quality control.

Contribution

The paper proposes an improved C-CenterNet model incorporating CBAM, deformable convolution, and ACON activation to enhance defect detection performance in cigarettes.

Findings

Achieved 95.01% mAP in cigarette defect detection.

Model success rate increased by 6.14% over original CenterNet.

Meets precision and adaptability requirements for industrial use.

Abstract

Due to the poor adaptability of traditional methods in the cigarette detection task on the automatic cigarette production line, it is difficult to accurately identify whether a cigarette has defects and the types of defects; thus, a cigarette appearance defect detection method based on C-CenterNet is proposed. This detector uses keypoint estimation to locate center points and regresses all other defect properties. Firstly, Resnet50 is used as the backbone feature extraction network, and the convolutional block attention mechanism (CBAM) is introduced to enhance the network's ability to extract effective features and reduce the interference of non-target information. At the same time, the feature pyramid network is used to enhance the feature extraction of each layer. Then, deformable convolution is used to replace part of the common convolution to enhance the learning ability of different shape defects. Finally, the activation function ACON (ActivateOrNot) is used instead of the ReLU activation function, and the activation operation of some neurons is adaptively selected to improve the detection accuracy of the network. The experimental results are mainly acquired via the mean Average Precision (mAP). The experimental results show that the mAP of the C-CenterNet model applied in the cigarette appearance defect detection task is 95.01%. Compared with the original CenterNet model, the model's success rate is increased by 6.14%, so it can meet the requirements of precision and adaptability in cigarette detection tasks on the automatic cigarette production line.