# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 1 Introduction
The model obtained a detection precision of up to 97.68% and also worked well in low-resolution and complex environments. [PERSON] et al. [11] focused on optimizing hyperparameters such as the optimizer, activation function, and loss function to improve the performance of U-Net in the anterior cruciate ligament (ACL) segmentation on magnetic resonance imaging (MRI) images, and finally adopted the Adam optimizer, sigmoid activation function, and loss function combining binary cross entropy and dice loss. The experimental results on the ACL MRI image dataset show that the improved U-Net obtains stable and outstanding results in several metrics such as precision and intersection over union (IoU), which provide valuable experience for improving other learning systems based on convolutional neural networks. Object detection methods based on convolutional neural networks are also widely used for crop pest detection [12]. According to division by the number of detection stages, this type of object detection method can be classified as either two-stage or one-stage [13]. Two-stage object detection methods split the detection process into the generation and detection of candidate boxes, while one-stage object detection methods detect objects directly through an end-to-end regression process. Two-stage object detection methods such as Mask R-CNN [14] were earlier applied to crop pest detection. [PERSON] et al. [15] designed a field pest detection method based on Mask R-CNN. To determine the proportion of semantic and spatial information in different levels of feature fusion, learnable weight coefficients were introduced for the feature fusion process of the Feature Pyramid Network (FPN) structure [16]. The method achieves up to 80.2% mAP, which is an improvement of 0.6% compared to the original Mask R-CNN, and far exceeds other methods such as Faster R-CNN. Such results fully demonstrate the importance of the reasonable fusion of semantic and spatial information. The FPN structure is widely used by learning systems in crop pest detection tasks to fuse features. [PERSON] et al. [17] similarly improved the FPN structure to expand the receptive field in the low layer and introduced bilinear interpolation for RoI pooling. These two improvements improved the detection of tiny pests. Meanwhile,Convolutional Block Attention Module (CBAM) was introduced into the backbone network to increase the targeting of feature extraction. The detection precision of the method is significantly improved compared with the original method, reaching up to 87.7% and demonstrating the effectiveness of the improvements. The complex structure of two-stage object detection methods leads to high training difficulty and slow inference, leading some researchers to turn to one-stage object detection methods such as SSD for crop pest detection. Based on the idea of feature fusion, widely used in two-stage object detection methods, [PERSON] et al. [18] designed a module for the SSD method that fuses semantic and spatial information between high and low layers to solve the problem of feature loss for small targets. Meanwhile, k-means clustering is used to generate more matching prior boxes for the self-constructed pest dataset. Experimental results on the dataset show that the improved SSD method has absolute advantages in terms of precision and speed, which demonstrates the improved effect of feature fusion and k-means. [PERSON] et al. [19] trained a YOLOv3 [20] model for detecting soybean pests. This model also used the FPN structure for feature fusion and k-means to generate better prior boxes. The model finally obtained a high precision of 95.15% and an F-score of 84.35% on the self-built INSECT12C soybean pest dataset, which can be used for practical soybean pest detection. The above CNN-based detection methods rely on prior boxes, and the generalizability is limited by the different requirements of prior boxes for different datasets.