Object Detection for Low-Illumination Scenes Based on Enhanced Yolov7 Architecture

This article proposes a novel approach to object detection in low-illumination conditions, an area where existing methods often fall short. By recognizing the crucial role of image quality in subsequent detection accuracy, we introduce preprocessing steps involving image enhancement and restoration networks. Our detection network then focuses on enhancing both channel and spatial feature information, integrating adaptive attention mechanisms and a Transformer architecture for contextual understanding. A multi-scale fusion strategy is employed to merge these features effectively. Additionally, we implement a partial cross-stage network to facilitate CNN learning while minimizing model size. Experimental results demonstrate the superiority of our approach over previous methods, showcasing significant accuracy improvements, particularly in challenging scenarios. This work underscores the importance of addressing image quality concerns in object detection tasks, especially in low-illumination environments, and offers a promising solution with practical implications across various domains, including autonomous driving and surveillance.