Recognition for Damaged Packages with GoogLeNet Based on Multi-strategy Optimization

WANG Meiling; MING Yanjiao; GAO Tong; SHEN Congcong; JU Hongmei

doi:10.19554/j.cnki.1001-3563.2026.05.029

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (5) : 272-287. DOI: 10.19554/j.cnki.1001-3563.2026.05.029

Green Packaging and Circular Economy

Recognition for Damaged Packages with GoogLeNet Based on Multi-strategy Optimization

WANG Meiling, MING Yanjiao, GAO Tong, SHEN Congcong, JU Hongmei^*

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Abstract

To address the limitations of traditional manual inspection, which is inefficient, costly, and difficult to scale, the work aims to propose a deep learning-based automatic package damage detection algorithm. The algorithm is primarily designed for logistics nodes such as sorting centers and transfer hubs. By analyzing images captured in real time before and after packages enter automated sorting lines, it enables real-time assessment of package integrity and provides a basis for subsequent actions such as interception, repair, or repackaging, thereby preventing further handling and transit of damaged packages and reducing potential losses. Data were collected from public datasets, web sources, and self-captured images, covering four conditions: intact, soiled, bent, and broken. Through a transfer learning approach, several mainstream deep learning models were compared, with GoogLeNet showing the best overall performance and thus selected as the baseline. The model was subsequently enhanced by integrating the CBAM attention mechanism, LeakyReLU activation, DropBlock regularization, a Bottleneck residual structure, and a redesigned classifier head. Further optimizations included label-smoothed cross-entropy loss, the AdamW optimizer, and a OneCycleLR learning rate scheduler. The improved GoogLeNet achieved a classification accuracy of 94.36% on the test set, with marked gains in Precision, Recall, and F1-Score over the baseline, while maintaining favorable efficiency in model size and inference time. These findings confirm the effectiveness and practicality of the proposed multi-strategy optimized GoogLeNet for package damage recognition, offering a reliable technical reference for intelligent logistics sorting and quality inspection in warehouses.

Key words

deep learning / package damage recognition / transfer learning / attention mechanism / GoogLeNet

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WANG Meiling, MING Yanjiao, GAO Tong, SHEN Congcong, JU Hongmei. Recognition for Damaged Packages with GoogLeNet Based on Multi-strategy Optimization[J]. Packaging Engineering. 2026, 47(5): 272-287 https://doi.org/10.19554/j.cnki.1001-3563.2026.05.029

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