| 低信噪比下基于YOLOv3的昆虫目标检测 |
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| 引用本文： | 梁文哲,冯阳凯,王锐,周超,蔡炯.低信噪比下基于YOLOv3的昆虫目标检测[J].信号处理,2022,38(1):109-117. |
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| 作者姓名： | 梁文哲 冯阳凯 王锐 周超 蔡炯 |
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| 作者单位： | 1.北京理工大学信息与电子学院，北京 100081 |
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| 基金项目： | 国家自然科学基金国家重大科研仪器研制项目(31727901)。 |
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| 摘 要： | 掌握昆虫迁飞规律对于农业防治和生态学研究具有重大意义,雷达正是检测昆虫迁飞最有效的手段.昆虫回波弱,传统的恒虚警检测(Constant False Alarm Rate,CFAR)算法在低信噪比(Signal To Noise Ratio,SNR)时的检测性能下降;同时昆虫目标体积小、飞行速度慢,在距离维和多普勒维的扩...
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| 关 键 词： | 昆虫目标检测 低信噪比 深度学习 |
| 收稿时间： | 2021-03-02 |
Insect Target Detection Based on YOLOv3 Under Low SNR |
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| LIANG Wenzhe,FENG Yangkai,WANG Rui,ZHOU Chao,CAI Jiong.Insect Target Detection Based on YOLOv3 Under Low SNR[J].Signal Processing,2022,38(1):109-117. |
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| Authors: | LIANG Wenzhe FENG Yangkai WANG Rui ZHOU Chao CAI Jiong |
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| Affiliation: | 1.Department of Information and Electronics,Beijing Institute of Technology, Beijing 100081, China2.Shanghai Satellite Engineering Research Institute, Shanghai 200240, China |
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| Abstract: | Mastering the rules of insect migration was of great significance to agricultural control and ecological research. Radar was the most effective way to detect insect migration. Beacause of Insects’ weak echoes， traditional Constant False Alarm Rate （CFAR） algorithm had poor detection performance under low signal-to-noise ratio （SNR）. At the same time， because insect targets were small in size， slow in flight speed， weak in range and Doppler dimensions and showed few features， recognition algorithms based on deep learning in the One-dimensional distance profile or range Doppler domain did not work well. In response to the problems， this paper proposed a insect target detection algorithm based on YOLOv3， which enrich image features of target by short-time Fourier transform. Using image features to identify insect targets improved the detection rate under low SNR. Moreover， false alarm-target dual training strategy and target detection confidence selecting strategy was used to reduce false alarm rate. The results of simulation and measured data show that the detection performance of the proposed algorithm is better than CA-CFAR under low SNR， which verifies the effectiveness of the algorithm. |
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| Keywords: | insect target detection low signal-to-noise ratio deep learning |
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