Fast Method of Detecting Packaging Bottle Defects Based on ECA-EfficientDet

Abstract

Conventional methods of detecting packaging defects face challenges with multiobject simultaneous detection for automatic filling and packaging of food. Targeting this issue, we propose a packaging defect detection method based on the ECA-EfficientDet transfer learning algorithm. First, we increased the complexity in the sampled data using the mosaic data augmentation technique. Then, we introduced a channel-importance prediction mechanism and the Mish activation function and designed ECA-Convblock to improve the specificity in the feature extractions of the backbone network. Heterogeneous data transfer learning was then carried out on the optimized network to improve the generalization capability of the model on a small population of training data. We conducted performance testing and a comparative analysis of the trained model with defect data. The results indicate that, compared with other algorithms, our method achieves higher accuracy of 99.16% with good stability.