LWSDNet: A Lightweight Wheat Scab Detection Network Based on UAV Remote Sensing Images

被引:2
作者
Yin, Ning [1 ]
Bao, Wenxia [1 ]
Yang, Rongchao [2 ]
Wang, Nian [1 ]
Liu, Wenqiang [1 ]
机构
[1] Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Hefei 230601, Peoples R China
[2] Zhengzhou Tobacco Res Inst CNTC, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金; 安徽省自然科学基金;
关键词
lightweight object detection; wheat scab; UAV remote sensing; feature fusion; knowledge distillation;
D O I
10.3390/rs16152820
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wheat scab can reduce wheat yield and quality. Currently, unmanned aerial vehicles (UAVs) are widely used for monitoring field crops. However, UAV is constrained by limited computational resources on-board the platforms. In addition, compared to ground images, UAV images have complex backgrounds and smaller targets. Given the aforementioned challenges, this paper proposes a lightweight wheat scab detection network based on UAV. In addition, overlapping cropping and image contrast enhancement methods are designed to preprocess UAV remote-sensing images. Additionally, this work constructed a lightweight wheat scab detection network called LWSDNet using mixed deep convolution (MixConv) to monitor wheat scab in field environments. MixConv can significantly reduce the parameters of the LWSDNet network through depthwise convolution and pointwise convolution, and different sizes of kernels can extract rich scab features. In order to enable LWSDNet to extract more scab features, a scab feature enhancement module, which utilizes spatial attention and dilated convolution, is designed to improve the ability of the network to extract scab features. The MixConv adaptive feature fusion module is designed to accurately detect lesions of different sizes, fully utilizing the semantic and detailed information in the network to enable more accurate detection by LWSDNet. During the training process, a knowledge distillation strategy that integrates scab features and responses is employed to further improve the average precision of LWSDNet detection. Experimental results demonstrate that the average precision of LWSDNet in detecting wheat scab is 79.8%, which is higher than common object detection models and lightweight object detection models. The parameters of LWSDNet are only 3.2 million (M), generally lower than existing lightweight object detection networks.
引用
收藏
页数:21
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