A Lightweight Infrared and Visible Image Fusion Method for Object Detection

A Lightweight Infrared and Visible Image Fusion Method for Object Detection

연구 분야: Verification

학회: International Forum on Digital TV and Wireless Multimedia Communications

Infrared images provide stable visual information in complex environments where visible light imaging performs poorly, such as in low light conditions and adverse weather. Many image fusion studies focus on fusing infrared and visible images to achieve complementary multi-source visual information, thereby enhancing overall image quality and information completeness. Meanwhile, with the fast-paced advancement of deep learning-driven technology, computer vision intelligent algorithms are increasingly replacing traditional human vision due to their high accuracy and real-time capabilities, undertaking a large amount of visual data analysis. Against this background, this paper selects object detection as a representative computer vision task and proposes a fusion method for infrared and visible images aimed at object detection with lightweight. The proposed approach is mainly composed of three components: feature extraction, feature fusion, and image reconstruction. During training, an object detection model is incorporated to optimize the fused image through object detection loss, thereby improving its adaptability and effectiveness for object detection tasks. Experimental results demonstrate that the proposed infrared and visible light fusion method is highly effective for object detection tasks, achieving better detection performance than using only infrared or visible light images. Additionally, the lightweight network model has low parameter complexity, which is well-suited for edge devices and real-time applications.