Efficient Parcel Damage Detection via Faster R-CNN: A Deep Learning Approach for Logistical Parcels’ Automated Inspection

Parcel damage detection poses a significant challenge in warehouse automation and conveyor belt transportation, with a particular focus on leveraging the potential of Internet of Things (IoT) technologies. With this purpose in mind, we introduce RPA R-CNN, an improved deep learning-based object detection method that seamlessly integrates IoT capabilities. Our proposed approach enhances the feature extraction network of the Faster R-CNN model and utilizes advanced post-processing techniques to further refine the extracted features. By employing a meticulously curated dataset specifically designed for parcel detection, we train our model and conduct rigorous experimental verification. Encouragingly, the outcomes convincingly demonstrate that the RPA R-CNN method surpasses the performance of the original model in various metrics in domestic or cross-border logistics parcel detection. These promising results not only offer a practical solution to the challenging problem of identifying parcel damage but also underscore the immense potential of integrating IoT principles to optimize automation and enhance operational efficiency within the domestic or cross-border logistics industry.