ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery
Unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras play a crucial role in combating nocturnal wildlife poaching. However, TIR images often face challenges such as jitter and wildlife overlap, necessitating UAVs to possess the capability to identify blurred and overlapping s...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10680397/ |
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| author | Ang He Xiaobo Li Ximei Wu Chengyue Su Jing Chen Sheng Xu Xiaobin Guo |
| author_facet | Ang He Xiaobo Li Ximei Wu Chengyue Su Jing Chen Sheng Xu Xiaobin Guo |
| author_sort | Ang He |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras play a crucial role in combating nocturnal wildlife poaching. However, TIR images often face challenges such as jitter and wildlife overlap, necessitating UAVs to possess the capability to identify blurred and overlapping small targets. Current traditional lightweight networks deployed on UAVs struggle to extract features from blurry small targets. To address this issue, we developed ALSS-YOLO, an efficient and lightweight detector optimized for TIR aerial images. First, we propose a novel adaptive lightweight channel split and shuffling (ALSS) module. This module employs an adaptive channel split strategy to optimize feature extraction and integrates a channel shuffling mechanism to enhance information exchange between channels. This improves the extraction of blurry features, crucial for handling jitter-induced blur and overlapping targets. Second, we developed a lightweight coordinate attention (LCA) module that employs adaptive pooling and grouped convolution to integrate feature information across dimensions. This module ensures lightweight operation while maintaining high detection precision and robustness against jitter and target overlap. Additionally, we developed a single-channel focus module to aggregate the width and height information of each channel into 4-D channel fusion, which improves the feature representation efficiency of infrared images. Finally, we modify the localization loss function to emphasize the loss value associated with small objects to improve localization accuracy. Extensive experiments on the BIRDSAI and ISOD TIR UAV wildlife datasets show that ALSS-YOLO achieves state-of-the-art performance. |
| format | Article |
| id | doaj-art-78792cd46a8e4d27b378a23d6c1f7fe0 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-78792cd46a8e4d27b378a23d6c1f7fe02024-11-16T00:00:10ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117173081732610.1109/JSTARS.2024.346117210680397ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV ImageryAng He0https://orcid.org/0009-0003-7204-7983Xiaobo Li1Ximei Wu2Chengyue Su3Jing Chen4https://orcid.org/0000-0001-5206-1110Sheng Xu5https://orcid.org/0000-0002-6314-8104Xiaobin Guo6https://orcid.org/0000-0003-0262-4063Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaGuangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, PR ChinaUnmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras play a crucial role in combating nocturnal wildlife poaching. However, TIR images often face challenges such as jitter and wildlife overlap, necessitating UAVs to possess the capability to identify blurred and overlapping small targets. Current traditional lightweight networks deployed on UAVs struggle to extract features from blurry small targets. To address this issue, we developed ALSS-YOLO, an efficient and lightweight detector optimized for TIR aerial images. First, we propose a novel adaptive lightweight channel split and shuffling (ALSS) module. This module employs an adaptive channel split strategy to optimize feature extraction and integrates a channel shuffling mechanism to enhance information exchange between channels. This improves the extraction of blurry features, crucial for handling jitter-induced blur and overlapping targets. Second, we developed a lightweight coordinate attention (LCA) module that employs adaptive pooling and grouped convolution to integrate feature information across dimensions. This module ensures lightweight operation while maintaining high detection precision and robustness against jitter and target overlap. Additionally, we developed a single-channel focus module to aggregate the width and height information of each channel into 4-D channel fusion, which improves the feature representation efficiency of infrared images. Finally, we modify the localization loss function to emphasize the loss value associated with small objects to improve localization accuracy. Extensive experiments on the BIRDSAI and ISOD TIR UAV wildlife datasets show that ALSS-YOLO achieves state-of-the-art performance.https://ieeexplore.ieee.org/document/10680397/Lightweight detectorsmall targetsthermal infrared (TIR)unmanned aerial vehicles (UAVs)wildlife detection |
| spellingShingle | Ang He Xiaobo Li Ximei Wu Chengyue Su Jing Chen Sheng Xu Xiaobin Guo ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Lightweight detector small targets thermal infrared (TIR) unmanned aerial vehicles (UAVs) wildlife detection |
| title | ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery |
| title_full | ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery |
| title_fullStr | ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery |
| title_full_unstemmed | ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery |
| title_short | ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery |
| title_sort | alss yolo an adaptive lightweight channel split and shuffling network for tir wildlife detection in uav imagery |
| topic | Lightweight detector small targets thermal infrared (TIR) unmanned aerial vehicles (UAVs) wildlife detection |
| url | https://ieeexplore.ieee.org/document/10680397/ |
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