A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution

Hongxia Yu; Lijun Yun; Zaiqing Chen; Feiyan Cheng; Chunjie Zhang

doi:10.1155/2023/2506274

A Small Object Detection Algorithm Based on Modulated Deformable Convolution and Large Kernel Convolution

Comput Intell Neurosci. 2023 Jan 24:2023:2506274. doi: 10.1155/2023/2506274. eCollection 2023.

Authors

Hongxia Yu¹, Lijun Yun^{1

2}, Zaiqing Chen¹, Feiyan Cheng¹, Chunjie Zhang¹

Affiliations

¹ College of Information, Yunnan Normal University, Kunming 650500, Yunnan, China.
² Yunnan Province Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming, Yunnan 650500, China.

Abstract

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.