Detection of Ectropis oblique in complex background images using improved YOLOv5

Abstract: Diseases and pests have posed a great threat to the yield and quality of tea in recent years. Among them, the Ectropis oblique is one of the most common pests in tea growth. A traditional detection has normally used the appearance of the pests, such as the color, morphology, and texture. But, these are more sensitive to the environments, particularly to the complex background, where the pests appear. A rapid and accurate detection cannot be realized, because: 1) The training samples are taken in different scales, while the pest is normally small in size; 2) The pest with the changeable shape and color may be shielded to obscure during imaging; 3) The color and texture of the pest can be similar to the tree branches and dead leaves of tea. Therefore, it is very necessary to identify and recognize the pest in a complex background in tea production. In this study, a rapid and accurate detection was proposed for the Ectropis oblique in complex background images using the improved YOLOv5 deep learning. Definitely, the YOLOv5 was taken as the baseline network. A labeling operation was first used to manually label the pest samples in the training and validation images. The data was then enhanced using the flipping, and contrast enhancement, particularly that the Gaussian noise was added to prevent data from overfitting. Meanwhile, the contrast of the test image was adjusted to reduce the influence of complex backgrounds, such as the tea pole on the detection of the scorpion. A convolution kernel group was also used to enhance the feature extraction without increasing the computation load. Furthermore, an attention module was utilized to adaptively adjust the receptive field, thereby enhancing the feature representation, according to the size and shape of the Ectropis oblique. More importantly, a Focal Loss function was used to reduce the impact of class imbalances between foreground and background during detection. The experimental results show that the convolution kernel group was effectively reduced the interference of complex background to the detection of tea geometrid. The attention module also presented an excellent performance to reduce the missed detection, due to the varying sizes and shapes of targets. Specifically, the best detection was achieved for the images with a complex background, where 0.94 recall, 0.96 precision, and 92.89% mean average precision. The improved accuracy increased by 6.44 percentage points, compared with the original YOLOv5. Moreover, there were 17.18 percentage points higher than the SSD, 6.52 percentage points higher than the Faster-RCNN, and 4.78 percentage points higher than the YOLOv4, compared with the SSD, Faster-RCNN, and YOLOv4. Consequently, the improved YOLOv5 can be widely expected to realize the intelligent monitoring of ectropis oblique pests in the precise pesticide application for the higher yield and quality of tea.