Remote sensing image objects detection algorithm based on improved YOLOv5

Junqi Shi; Lei Li; Fuxiang Liu; Chunfeng Xu

doi:10.1117/12.2652220

10 November 2022 Remote sensing image objects detection algorithm based on improved YOLOv5

Junqi Shi, Lei Li, Fuxiang Liu, Chunfeng Xu

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123314K (2022) https://doi.org/10.1117/12.2652220
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

With the development of remote sensing technology, remote sensing images are developing towards higher resolution and larger data volume, and it is important to identify objects from remote sensing images quickly and accurately. Due to the significant differences between remote sensing images and natural images, the direct application of existing algorithms to remote sensing images is not ideal. Therefore, we take the YOLOv5 object detection algorithm as base, and have completed the following tasks: First, we present an adaptive image cutting data preprocessing method, which fills or cuts images into uniform size to cope with the resolution differences of remote sensing images. Second, we use the Mosaic data enhancement method to improve the algorithm's effect in complex backgrounds. Third, we use the Soft-NMS postprocessing algorithm to reduce the missed detection of dense objects. Furthermore, we transplant the algorithm to the hardware platform. After the above improvements, the mAP of our algorithm increases from 0.354 to 0.677 on the DOTA dataset, achieving a good object detection effect on remote sensing images; and with the help of the TensorRT, it has reached a detection speed of about 60 FPS on the NVIDIA Jetson AGX Xavier embedded hardware platform.

Citation Download Citation

Junqi Shi, Lei Li, Fuxiang Liu, and Chunfeng Xu "Remote sensing image objects detection algorithm based on improved YOLOv5", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123314K (10 November 2022); https://doi.org/10.1117/12.2652220

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