Feature coordination and fine-grained perception of small targets in remote sensing images

Xiao Zhenjiu; Zhang Jiehao; Lin Bohan

doi:10.12086/oee.2024.240066

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Xiao Z J, Zhang J H, Lin B H. Feature coordination and fine-grained perception of small targets in remote sensing images[J]. Opto-Electron Eng, 2024, 51(6): 240066. doi: 10.12086/oee.2024.240066

Citation:

Xiao Z J, Zhang J H, Lin B H. Feature coordination and fine-grained perception of small targets in remote sensing images[J]. Opto-Electron Eng, 2024, 51(6): 240066. doi: 10.12086/oee.2024.240066

Feature coordination and fine-grained perception of small targets in remote sensing images

School of Software, Liaoning University of Engineering and Technology, Huludao, Liaoning 125105, China

Fund Project: Project supported by Basic Scientific Research Project of Liaoning Provincial Universities (LJKMZ20220699), and Subject Innovation Team Project of Liaoning Technical University (LNTU20TD-23)

More Information

^*Corresponding author: zjhao0409@163.com

Received Date 20 March 2024

Revised Date 25 April 2024

Accepted Date 26 April 2024

Published Date 25 June 2024

Abstract

Abstract

Addressing the challenge of missed detection caused by many small targets and dense arrangement in remote sensing images, this study introduces a small target detection algorithm for remote sensing applications, leveraging a combination of feature synergy and micro-perception strategies. Initially, we propose a refined feature synergistic fusion strategy that optimizes the interaction and integration of features across different scales by intelligently adjusting the parameters of convolution kernels. This strategy facilitates progressive refinement of features from coarse to fine granularity. Building upon this foundation, a micro-perception unit is developed in this paper, incorporating perceptual attention mechanisms with moving inverse convolution to form an advanced detection head. This innovative approach substantially boosts the network's capability to detect very small objects. Furthermore, to augment the training efficiency of the model, we employ MPDIoU and NWD as regression loss functions, mitigating positional bias issues and expediting model convergence. Experimental evaluations on the DOTA1.0 dataset and DOTA1.5 dataset reveal that our algorithm achieves a substantial improvement in mean Average Precision (mAP) by 7.4% and 6.1% over the baseline method, which has obvious advantages over other algorithms. The results underscore the algorithm's efficacy in significantly reducing the incidence of missed detections of small targets within remote sensing imagery.
- remote sensing images /
- small target detection /
- feature synergy /
- fine-gained aware

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References

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Overview

Overview

With the rapid development of remote sensing image technology, remote sensing image target detection is widely used in many important fields, including military target location and identification, natural environment protection, disaster detection, and urban planning and construction. The task of remote sensing image target detection is to accurately identify and locate the specific target in the image, and speculate its type and position. Different from targets in natural scenes, targets in remote sensing images have the characteristics of large scenes, small targets, multi-scale, complex backgrounds, overlapping occlusion, etc., so it is a challenging task to detect specific objects accurately. At present, great breakthroughs have been made in remote sensing image target detection algorithms, but the effect of small target detection is still not ideal. Small target detection faces two major difficulties: Little feature information of the target, scarce positive samples, and unbalanced classification; The target location is difficult, the background is complex, and contains a lot of redundant information, which causes serious interference to the detection. This makes it challenging to extract the edge features from aerial images and distinguish the object from the background. Therefore, the research on object detection and application in remote sensing images has important theoretical and practical significance. Addressing the challenge of missed detection caused by many small targets and dense arrangement in remote sensing images, this study introduces a small target detection algorithm for remote sensing applications, leveraging a combination of feature synergy and micro-perception strategies. Initially, we propose a refined feature synergistic fusion strategy that optimizes the interaction and integration of features across different scales by intelligently adjusting the parameters of convolution kernels. This strategy facilitates progressive refinement of features from coarse to fine granularity. Building upon this foundation, a micro-perception unit is developed in this paper, incorporating perceptual attention mechanisms with moving inverse convolution to form an advanced detection head. This innovative approach substantially boosts the network's capability to detect very small objects. Furthermore, to augment the training efficiency of the model, we employ MPDIoU and NWD as regression loss functions, mitigating positional bias issues and expediting model convergence. Experimental evaluations on the DOTA1.0 dataset and DOTA1.5 dataset reveal that our algorithm substantially improves mean Average Precision (mAP) by 7.4% and 6.1% over the baseline method, which has obvious advantages over other algorithms. The results underscore the algorithm's efficacy in significantly reducing the incidence of missed detections of small targets within remote sensing imagery.