基于空间金字塔池化和深度卷积神经网络的作物害虫识别

为了减少因作物害虫姿态多样性和尺度多样性导致其识别精度相对较低的问题,该文将空间金字塔池化与改进的YOLOv3深度卷积神经网络相结合,提出了一种基于空间金字塔池化的深度卷积神经网络农作物害虫种类识别算法,首先对测试图像上的害虫进行检测定位,然后对检测定位出的害虫进行种类识别。通过改进YOLOv3的网络结构,采用上采样与卷积操作相结合的方法实现反卷积,使算法能够有效地检测到图片中体型较小的作物害虫样本;通过对采集到的实际场景下20类害虫进行识别测试,识别精度均值可达到88.07%。试验结果表明,本文提出的识别算法能够有效地对作物害虫进行检测和种类识别。

Crop pest identification based on spatial pyramid pooling and deep convolution neural network

Abstract: Traditional methods for classifying crop pests are based on the appearance of the pests such as their color, morphology and texture using algorithms s back propagation neural network and support vector machine. These methods are sensitive to the environments where the pests appear. Also, the imbalance between training sample numbers, difference in scales at which the training samples are taken, along with that the pests might seasonally change their color and shape, make it difficult for these algorithms to detect and recognize the pests. In order to improve the accuracy of detecting crop pests appearing in diverse environments, this paper proposed a deep convolutional neural network method by combining the spatial pyramid pooling with the improved YOLOv3 deep convolutional neural network algorithm. The proposed method first located the pests on a test image and then identified the species they belongs to. Using the improved network structure in YOLOv3, the proposed method used up-sample and convolution operations to calculate the de-convolution. Bilinear interpolation was used to enhance the output of the network, and the depth characteristics of the pests were extracted by the depth residual neural network. As a result, the improved network can effectively detect and recognize small pests in the images. By fusing with the spatial pyramid pooling, the Yolov3-SPP network can map the extracted eigenvectors to different spatial dimensions. The network can be used at various scales to detect pests of different sizes. Results from identifying and testing 20 types of pests collected by traps showed that the average accuracy of the proposed method was 88.07%, with a detection speed of 26 frames/s. Compared with the typical YOLOv3 algorithm, the proposed methods improved accuracy by 2.8 percentage points. We also reconstructed the maps of the network at each stage and compared them with the typical YOLOv3 algorithm in attempts to demonstrate that the features extracted by the proposed method was more recognizable. The results revealed that the typical YOLOv3 algorithm could miss the targeted objects, while the proposed method is not only able to detect pests of different sizes, it also has high recognition accuracy. Using the same dataset, we compared the proposed method with other detection algorithms such as HOG+SVM, Faster R-CNN, YOLOv3. The comparison showed that the proposed method was 19.61 percentage points higher than that of HOG + SVM, and 9.64 percentage points higher than that of Faster R-CNN, Faster R-CNN was unable to extract small pests, and its recognition accuracy was only 78.43%.