Enhanced YOLOv8 for Efficient Parcel Identification in Disordered Logistics Environments

Accurate parcel identification in disordered logistics environments poses significant challenges due to varying package sizes, materials, and orientations. This study presents an improved YOLOv8-Efficiency algorithm tailored for such complex scenarios. The proposed algorithm introduces the C2f-OR module to reduce parameters and computation, the Conv-Ghost module for efficient feature extraction, and the HIoU loss function to enhance identification accuracy. By constructing a dataset of 4689 photos, experiments demonstrate the algorithm's effectiveness, achieving a 93.2% mAP, a 1.6% recall rate improvement, and a significant reduction in computational complexity (9.9% decrease in FLOPs). This work provides a robust solution for real-time parcel identification in disordered logistics, facilitating automation and efficiency in logistics operations.