Mei Mengqing, Zhou Ziyu, Liu Wei, Ye Zhiwei
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel). 2024 Sep 5;24(17):5787. doi: 10.3390/s24175787.
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.
在计算机视觉领域，目标检测至关重要，并且在各种场景中都展现出了值得称赞的性能。然而，它通常需要场景内有良好的可见度条件。因此，探索在低可见度情况下进行目标检测的方法势在必行。凭借其速度与准确性的平衡组合，先进的YOLOv8框架已被公认为目标检测的顶级算法之一，在一系列标准数据集上都展示出了出色的性能结果。尽管如此，当前的YOLO系列检测算法在低光照条件下检测目标时仍面临重大挑战。这主要是因为在光照充足的数据上训练的检测器应用于可见度有限的低光照数据集时，性能会显著下降。为了解决这个问题，我们基于YOLOv8架构提出了一种名为分组偏移和孤立长颈鹿检测目标检测 - YOLO的新模型。所提出的模型在低光照条件下表现出卓越的性能。我们在特征融合层颈部的设计中采用repGFPN特征金字塔网络，以增强分层融合并加深低光照信息的整合。此外，我们通过引入采样图偏移来改进repGFPN特征融合层，以解决其在权重和效率方面的局限性，从而使其更好地适应低光照环境下的实时应用，并突出此类场景的潜在特征。此外，我们利用分组卷积将检测到的物体边缘的干扰信息隔离开来，从而提高检测性能和模型效率。实验结果表明，与YOLOv8相比，我们的GOI - YOLO减少了11%的参数数量，同时将计算需求降低了28%。这种优化显著提高了实时性能，同时在ExDark数据集上实现了Map50竞争力提升2.1%，Map95竞争力提升0.6%。
