YOLOv5-Based Model Integrating Separable Convolutions for Detection of Wheat Head Images

In the detection of global wheat heads, it is easy to give rise to difficulties due to different wheat varieties, planting densities and growth periods of wheat plants in different countries. In addition, the illumination conditions of the image collection and the complex background of field will also reduce the detection accuracy. It is also hard to accurately detect targets that are occluded and partially displayed in the image. To solve the above problems, in this paper, an improved YOLOv5 algorithm that integrates separable convolution and attention mechanisms is proposed. Firstly, the number of CSP modules of YOLOv5 is reduced to shrink memory consumption. Subsequently, vanilla convolutions in the CSP are replaced by separable convolutions which is also added to the fusion path and to reduce the redundant information of the feature map, so as to reduce the complexity of the model. In addition, the co-attention mechanism is added in backbone. Finally, the feature fusion module was adjusted to make the high-level features fuse more low-level information. Compared with the original algorithm, results show that the mAP of the improved algorithm reaches 93.8% which is 4.2% higher than that of the YOLOv5 algorithm, and the FPS is 27.4 which is 1.3 higher than YOLOv5. YOLOv7 is emphatically compared during model evaluation, other YOLO series and mainstream detection algorithms are also compared, and results show that our model has the best inference time and the best accuracy when dealing with high pixel images.