Abstract
Aiming at the difficulty of small-scale objects in high-resolution remote sensing images, this paper proposes a new detector DA-YOLO (dilation and attention YOLO) to locate objects quickly and accurately. Firstly, during the data preprocessing, the remote sensing images processed by “quadruple cropping” to adjust the original image size and enlarge the number of data instance. Then, the CSPDarknet53 backbone network is optimized: the dilated separable convolution (DSC) module is applied to enlarge the receptive range of feature maps without losing the resolution of feature maps. Then, the convolutional block attention module (CBAM) is introduced for feature enhancement, and finally, the last four stages of feature maps are used instead of three stages to obtain more contour details of small-scale objects. Extensive experiments show that DA-YOLO has good performance in DOTA, with a 2.36% increase in mAP compared to the original YOLOv4 without a significant decrease in detection speed.
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Hu, R., Ting, R. (2023). An Object Detection Method for Remote Sensing Images Based on DA-YOLO. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_13
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DOI: https://doi.org/10.1007/978-981-99-0923-0_13
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