E-Commerce Image Enhancement Method Based on Instance Segmentation and Background Replacement

In the field of e-commerce, the visual presentation of product images is crucial for attracting consumers, improving conversion rates, and enhancing user experience. However, existing image enhancement methods often struggle to balance high-quality visual effects with computational efficiency, especially when handling complex and diverse datasets. This paper proposes a novel image enhancement method that integrates instance segmentation, dominant color detection, background replacement, realistic shadow generation, and logo addition into a unified framework, optimized for e-commerce product images. The method begins with instance segmentation, effectively separating foreground products from the background to provide clear targets for further processing. Dominant color detection ensures visual consistency by extracting the primary colors of the product images. Background replacement techniques improve the aesthetic appeal by replacing the original background with more suitable or attractive scenes. The addition of realistic shadows enhances the three-dimensional appearance of the product, while logo integration strengthens branding and recognition. Experimental results demonstrate that the proposed method significantly improves recognition accuracy, IoU, and mAP for models such as YOLOv5, SSD, and Faster-RCNN, with YOLOv5 showing improvements of 16.66%, 23.28%, and 24.32%, respectively. With an average processing time of 125 ms per image, the method offers a superior balance between performance and computational efficiency, making it suitable for real-time e-commerce applications. The method also holds promise for other domains, including natural landscape photography, medical imaging, and artwork analysis. Future work will incorporate statistical analyses and extend the dataset to include more diverse product categories, aiming to further validate the generalizability and scalability of the method.