Intelligent Detection of Parcels Based on Improved Faster R-CNN

Abstract

Parcel detection is crucial to achieving automatic sorting in intelligent logistics systems. Most parcels in logistics centers are currently detected manually, imposing low efficiency and high error rate, severely limiting logistics transportation efficiency. Therefore, there is an urgent need for automated parcel detection. However, parcels in logistics centers have challenges such as dense stacking, occlusion and background interference, making it difficult for existing methods to detect parcels accurately. To address the above problem, we developed an improved Faster R-CNN-based parcel detection model spurred by current deep-learning-based object detection trends. The proposed method first solves the false detection problem due to parcel mutual occlusion by augmenting Faster R-CNN with an edge detection branch and adding object edge loss to the loss function. Furthermore, the self-attention ROI Align module is proposed to address the problem of feature misalignment caused by the quantization rounding operation in the ROI Pooling module. The module uses an attention mechanism to filter and enhance the features and uses bilinear interpolation to calculate the feature pixel values, improving detection accuracy. The implementation of the proposed method was validated using parcel images collected in the field and the public dataset SKU110K and compared with four existing parcel detection methods. The results show that our method’s Recall, Precision, map@0.5 and Fps are 96.89%, 98.76%, 98.42% and 22.83%, respectively, which significantly improves the parcel detection accuracy.