A Multi-Scale Deformable Convolutional Neural Network with Adaptive Adjustment for Robust Packaging Image Recognition

Abstract

This paper presents an innovative image recognition model for package inspection, designed to fulfill the demands of real-time and precise categorization in difficult industrial environments. Traditional techniques reliant on human feature extraction frequently underperform when confronted with lighting variability, background interference, and deformation of package items. To mitigate these limitations, the proposed model integrates a multi-scale convolutional architecture that captures both local and global characteristics through the use of parallel convolutional filters of varying sizes. An adaptive adjustment method is incorporated into the network to dynamically alter the placement of convolutional operations according to image content, hence improving flexibility and feature representation. A thorough data augmentation strategy incorporating geometric transformation, brightness modification, and semantic-level blending is implemented to boost the model's robustness and generalization capacity. Experiments performed on a bespoke industrial packaging dataset comprising 10,000 labeled images reveal that the proposed model attains a classification accuracy of 96.8 percent, a recall of 95.3 percent, and an F1-score of 93.8 percent, with an inference time of 11.2 milliseconds and a parameter count of 21.3 million. In comparison to current deep learning architectures like Residual Networks, the model demonstrates considerable enhancements in accuracy and speed. These results support its appropriateness for practical packaging inspection systems.