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Given the rapid progress of artificial intelligence technology, object detection, as a significant research direction in the realm of computer vision, has made remarkable progress. However, existing algorithms still face challenges in terms of insufficient detection accuracy, poor robustness, and difficulties in balancing model performance and efficiency in complex backgrounds. To address these issues, this study proposes an innovative object detection algorithm, CGR-YOLO. This algorithm introduces Context Guided down-sampling technology based on YOLOv8s, which effectively filters out irrelevant information and retains valuable data by comprehensively considering the surrounding contextual information, thus markedly enhancing the model's detection accuracy. Furthermore, the CGR-YOLO algorithm employs Repconv to substitute the conventional convolution operations in the Bottleneck module, combining re-parameterization techniques, which not only enhances the model's operational efficiency but also improves overall performance. A sequence of experiments conducted on the Pascal VOC dataset confirms the superiority of the CGR-YOLO algorithm. Specifically, CGR-YOLO improves the mAP50 accuracy by 1.8% and the mAP50-95 accuracy by 2.2% compared to YOLOv8s. These results show that the CGR-YOLO algorithm provides higher accuracy in the object detection domain while enhancing the robustness and accuracy of the model, which provides an effective solution to the limitations of existing algorithms.
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