Defect detection of biscuit packaging based on level set map

Biscuit packaging must be thoroughly inspected after being sealed. However, biscuit packaging defect detection remains a challenging task due to both subtle and complex textures at the seal and the lack of defective samples. To overcome these difficulties, we propose a multi-task defect detection framework based on level set map. First, level set map (LSM), a new segmentation task label is proposed, which can represent both contour information and defect location information by using image gray value. Then, a multi-task framework based on LSM is designed, the main task of which is the binary classification task to predict the defect state of the packaging, and the auxiliary task is the semantic segmentation task of extracting biscuit packaging contours and locating defects. The two tasks share the feature extractor, and the auxiliary task provides a supervised spatial attention to guide the feature extractor to focus on the contour of the packaging. To verify the performance of the multi-task framework, two real datasets under different acquisition environments are established. The experimental results show that, compared with other classification networks and object detection framework, the multi-task framework based on LSM can significantly improve the accuracy of the biscuit packaging defect detection task.