SPD-YOLO: A Method for Detecting Maize Disease Pests Using Improved YOLOv7

In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the Space-to-Depth Convolution (SPD-Conv) module into the SPD-YOLOv7 framework, replacing certain convolutional layers in the traditional system backbone and head network. This modification helps retain small target features and location information. Additionally, the Efficient Layer Aggregation Network-Wide (ELAN-W) module is combined with the Convolutional Block Attention Module (CBAM) attention mechanism to extract more efficient features. Experimental results show that the enhanced YOLOv7 model achieves an accuracy of 98.38%, with an average accuracy of 99.4%, outperforming the original YOLOv7 model. These improvements represent an increase of 2.46% in accuracy and 3.19% in average accuracy. The results indicate that the enhanced YOLOv7 model is more efficient and real-time, offering valuable insights for maize pest control.