Remote sensing object detection based on dilated convolution and multi-scale feature fusion

Haoran Yin; Chuan Xu

doi:10.1117/12.2681540

1 June 2023 Remote sensing object detection based on dilated convolution and multi-scale feature fusion

Haoran Yin, Chuan Xu

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181H (2023) https://doi.org/10.1117/12.2681540
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Aiming at the problem of poor detection results due to the large difference in scale of detection targets and numerous small targets in remote sensing images, a remote sensing target detection algorithm DCB-YOLO based on atrous convolution and multi-scale feature fusion is proposed. Based on the YOLOv5 algorithm, the algorithm designs a bidirectional feature pyramid structure (MDCB-BiFPN) with multi-branch hole convolution. The MDCB-BiFPN structure can better extract and fuse features of different scales, thereby improving the model's ability to detect multi-scale remote sensing targets. The experimental results on the remote sensing public dataset DIOR show that the DCB-YOLO algorithm can better accurately identify remote sensing targets at different scales.

Citation Download Citation

Haoran Yin and Chuan Xu "Remote sensing object detection based on dilated convolution and multi-scale feature fusion", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181H (1 June 2023); https://doi.org/10.1117/12.2681540

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KEYWORDS

Target detection

Convolution

Remote sensing

Detection and tracking algorithms

Feature fusion

Object detection

Feature extraction

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