Robust SCM Packaging Authentication through Aggregation-Based CSP Darknet53 with Integrated Spatial Pyramid Pooling
DOI:
https://doi.org/10.52756/ijeim.2025.v01.i02.005Keywords:
Darknet, DNN, Object Detection, Packaging Authentication, Spatial Pyramid PoolingAbstract
The Supply chain business model is being reshaped and transformed by artificial intelligence (AI) using contemporary, environmentally friendly AI-based techniques. Customers frequently receive the incorrect items in parcel boxes when they order online. Supply Chain (SC) companies uphold a simple return policy for clients, which incurs additional expenses for a specific order, to preserve their reputation. A certain amount of time is spent on the return procedure, which can result in losses for significant returns. By incorporating deep learning algorithms into the SC packaging process, the suggested method attempts to address this issue. Before the things are delivered, the darknet architecture in this document detects incorrect product delivery. Semantic properties are extracted from the video footage using the following framework. The suggested method is made with CSPDarknet53 to enhance learning capacity. The spatial pyramid pooling candidate window preserves 6×6 features as a connection parameter while providing special information processing to the various CNN layers. The DPM model is used for the encoding process. In the suggested information construction, a selective, optimistic search pattern path augmentation localized by RELU activation is used to choose features.
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