RecNet: Optimization for Dense Object Detection in Retail Scenarios Based on View Rectification

Abstract

High-precision dense object detection in retail is crucial for automation, inventory management, and sales optimization. Our experiments revealed that detection models perform significantly better with frontal views than with oblique views, motivating the development of RecNet. RecNet utilizes a Rectify-Detect (R-D) pipeline to transform oblique views into frontal views, mitigating perspective distortion and focusing on key regions. To optimize bounding box prediction, we propose the CeIoU loss function, which focuses on high-quality boxes using a fair elite selection strategy. We also introduce the Neighbor Scattering Algorithm to address accuracy loss caused by rounding errors during the rectification process. Additionally, we present a Transform-Aware Branch that integrates transformation information into the regression branch for direct bounding box prediction. Experiments show that RecNet achieves state-of-the-art performance on SKU110K and demonstrates strong generalization on PUCPR+.