Kang Chenrui, Jiao Lin, Wang Rujing, Liu Zhigui, Du Jianming, Hu Haiying
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Insects. 2022 Oct 25;13(11):978. doi: 10.3390/insects13110978.
A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper. Firstly, a large-scale corn pest dataset has been constructed which includes 7741 corn pest images with 10 classes. Secondly, a deep residual network with deformable convolution has been introduced to obtain the features of the corn pest images. To address the detection task of multi-scale corn pests, an attention-based multi-scale feature pyramid network has been developed. Finally, we combined the proposed modules with a two-stage detector into a single network, which achieves the identification and localization of corn pests in an image. Experimental results on the corn pest dataset demonstrate that the proposed method has good performance compared with other methods. Specifically, the proposed method achieves 70.1% mean Average Precision (mAP) and 74.3% Recall at the speed of 17.0 frames per second (FPS), which balances the accuracy and efficiency.
一场严重的农业害虫爆发导致玉米产量大幅损失。因此，在早期预警期间进行准确且可靠的玉米害虫检测非常重要，这可以预防玉米害虫造成的损害。为了实现对玉米害虫的准确检测，本文引入了一种基于卷积神经网络的新方法。首先，构建了一个大规模的玉米害虫数据集，其中包括7741张具有10个类别的玉米害虫图像。其次，引入了具有可变形卷积的深度残差网络来获取玉米害虫图像的特征。为了解决多尺度玉米害虫的检测任务，开发了一种基于注意力的多尺度特征金字塔网络。最后，我们将所提出的模块与两阶段检测器组合成一个单一网络，实现了图像中玉米害虫的识别和定位。在玉米害虫数据集上的实验结果表明，与其他方法相比，所提出的方法具有良好的性能。具体而言，所提出的方法在每秒17.0帧（FPS）的速度下实现了70.1%的平均精度均值（mAP）和74.3%的召回率，平衡了准确性和效率。
