College of Information, Yunnan Normal University, Kunming 650500, Yunnan, China.
Yunnan Province Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming, Yunnan 650500, China.
Comput Intell Neurosci. 2023 Jan 24;2023:2506274. doi: 10.1155/2023/2506274. eCollection 2023.
Object detection is one of the most critical areas in computer vision, and it plays an essential role in a variety of practice scenarios. However, small object detection has always been a key and difficult problem in the field of object detection. Therefore, considering the balance between the effectiveness and efficiency of the small object detection algorithm, this study proposes an improved YOLOX detection algorithm (BGD-YOLOX) to improve the detection effect of small objects. We present the BigGhost module, which combines the Ghost model with a modulated deformable convolution to optimize the YOLOX for greater accuracy. At the same time, it can reduce the inference time by reducing the number of parameters and the amount of computation. The experimental results show that BGD-YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0.5 up to 88.3% and mAP0.95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.
目标检测是计算机视觉中最关键的领域之一，它在各种实际场景中都起着至关重要的作用。然而，小目标检测一直是目标检测领域的一个关键和难题。因此，考虑到小目标检测算法的有效性和效率之间的平衡，本研究提出了一种改进的 YOLOX 检测算法（BGD-YOLOX），以提高小物体的检测效果。我们提出了 BigGhost 模块，它将 Ghost 模型与调制变形卷积相结合，以优化 YOLOX 以获得更高的准确性。同时，它可以通过减少参数数量和计算量来减少推理时间。实验结果表明，BGD-YOLOX 在小目标检测方面具有更高的平均准确率，mAP0.5 高达 88.3%，mAP0.95 高达 56.7%，超过了最先进的目标检测算法，如 EfficientDet、CenterNet 和 YOLOv4。
