Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the actual parcel-transportation process. For this purpose, 1) the study acquired image data in an environment simulating the actual parcel-transportation process, and 2) the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation. Additionally, 3) a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage. Finally, using the dataset constructed based on the proposed method, a damage type detection model was trained, and its mean average precision was confirmed. This model can improve customer satisfaction and reduce return costs for parcel delivery companies.

This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems. The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints. Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions, if possible to be physically implemented. Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability. Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments. This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry, with potential applications in last-mile delivery services, warehousing, and supply chain management.