Integration of Multiscale Fusion and Cross-scale Attention Refinement for Enhanced Target Detection Using MSFNet - Liao, Xiaofang; Liu, Xinnan (Informatica, 2025)
Object recognition across varying scales remains a persistent challenge in computer vision, especially in scenes with occlusion, low contrast, and diverse spatial resolutions. Conventional convolutional neural networks with fixed receptive fields often fail to capture both fine-grained details and high-level contextual cues. This study focuses on developing a scale-adaptive detection framework to overcome these limitations. The proposed MSFNet (Multiscale Fusion Network) employs a Dual-Stream Convolutional Backbone to extract low-level and high-level features in parallel. A Scale-Adaptive Feature Fusion Module (SAFFM) integrates multiscale representations through dynamic, scale-aware weighting. A Cross-Scale Attention Refinement (CSAR) module enhances discriminative features and suppresses irrelevant or redundant information. The architecture operates in an end-to-end fashion and is optimized for detection accuracy and real-time inference speed. Experimental evaluation on MS COCO 2017 and PASCAL VOC 2012 reports 47.3% AP and 81.5% mAP, respectively. Performance exceeds Faster R-CNN, YOLOv5, and RetinaNet by +3.8%, +4.5%, and +3.2% AP on the COCO benchmark. MSFNet provides a scalable, accurate, and computationally efficient approach for multiscale object recognition, enabling deployment in real-time applications such as autonomous driving, intelligent surveillance, and remote sensing.