Multi-receptive-field-based remote sensing object detection technology

Kuangyin Meng; Mingzheng Wang; Kai Fu; Changbin Shao

doi:10.1117/12.2678870

25 May 2023 Multi-receptive-field-based remote sensing object detection technology

Kuangyin Meng, Mingzheng Wang, Kai Fu, Changbin Shao

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 1271214 (2023) https://doi.org/10.1117/12.2678870
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

The current mainstream object detection algorithms are designed based on natural distribution images, which results in low detection accuracy when detecting remote sensing data images with a large number of densely packed small targets. Traditional deep detection networks mostly use 3x3 convolution kernels, which makes the receptive field of the network relatively fixed, and is not conducive to detecting remote sensing targets with large scale differences and dense distribution. Inspired by the receptive field (RF) structure in the human visual system, we propose to design feature extraction modules with different receptive fields in the deep neural network, which are used to collect the spatial information of the feature maps. This method takes the multi-branch convolution module in Inception V4 as a reference idea, and uses dilated convolution to enhance the receptive field and improve the extraction ability for densely packed targets. The operation of having different receptive fields for the network provides a more refined resolution ability for the model. To verify its effectiveness, we conducted experimental comparisons on multiple datasets based on YOLOX as the benchmark model, and the experimental results confirmed the effectiveness of our multi-receptive-field approach.

Citation Download Citation

Kuangyin Meng, Mingzheng Wang, Kai Fu, and Changbin Shao "Multi-receptive-field-based remote sensing object detection technology", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 1271214 (25 May 2023); https://doi.org/10.1117/12.2678870

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KEYWORDS

Object detection

Convolution

Education and training

Feature extraction

Detection and tracking algorithms

Remote sensing

Target detection

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