GOI-YOLOv8 Grouping Offset and Isolated GiraffeDet Low-Light Target Detection

In the realm of computer vision, object detection holds significant importance and has demonstrated commendable performance across various scenarios. However, it typically requires favorable visibility conditions within the scene. Therefore, it is imperative to explore methodologies for conducting object detection under low-visibility circumstances. With its balanced combination of speed and accuracy, the state-of-the-art YOLOv8 framework has been recognized as one of the top algorithms for object detection, demonstrating outstanding performance results across a range of standard datasets. Nonetheless, current YOLO-series detection algorithms still face a significant challenge in detecting objects under low-light conditions. This is primarily due to the significant degradation in performance when detectors trained on illuminated data are applied to low-light datasets with limited visibility. To tackle this problem, we suggest a new model named Grouping Offset and Isolated GiraffeDet Target Detection-YOLO based on the YOLOv8 architecture. The proposed model demonstrates exceptional performance under low-light conditions. We employ the repGFPN feature pyramid network in the design of the feature fusion layer neck to enhance hierarchical fusion and deepen the integration of low-light information. Furthermore, we refine the repGFPN feature fusion layer by introducing a sampling map offset to address its limitations in terms of weight and efficiency, thereby better adapting it to real-time applications in low-light environments and emphasizing the potential features of such scenes. Additionally, we utilize group convolution to isolate interference information from detected object edges, resulting in improved detection performance and model efficiency. Experimental results demonstrate that our GOI-YOLO reduces the parameter count by 11% compared to YOLOv8 while decreasing computational requirements by 28%. This optimization significantly enhances real-time performance while achieving a competitive increase of 2.1% in Map50 and 0.6% in Map95 on the ExDark dataset.