Blueberry maturity recognition method based on improved YOLOv4-Tiny

Abstract: The accurate identification of blueberry fruit maturity is very important for modern automatic picking and early yield estimation. To realize the accurate and rapid identification of blueberry fruit in the natural environment, by improving the structure of YOLOv4-Tiny network, a target detection network with attention module (I-YOLOv4-Tiny) was proposed. The detection network used CSPDarknet53-Tiny network model as the backbone network, and the convolution block attention module (CBAM) was added to the feature pyramid network (FPN) model. Feature compression, weight generation and reweighting were carried out on the feature channel dimension and feature space dimension of the target detection network, The two dimensions of channel attention and spatial attention selectively integrated the deep and shallow features. High order features guided low-order features for channel attention acquisition, and low-order features reversed guide high-order features for spatial attention screening, which could improve the feature extraction ability of network structure without significantly increasing the amount of calculation and parameters, and realized the real-time detection performance of network structure, the correlation of features between different channels was learned by weight allocation of features of each channel, and the transmission of deep information of network structure was strengthened, to reduce the interference of complex background on target recognition. Moreover, the detection network has fewer network layers and low memory consumption, to significantly improve the accuracy and speed of blueberry fruit detection. The performance evaluation and comparative test results of the research recognition method showed that the Mean Average Precision (mAP) of the trained I-YOLOv4-Tiny target detection network under the verification set was 97.30%, which could effectively use the color images in the natural environment to identify blueberry fruits and detect fruit maturity. The average accuracy and F1 score of I-YOLOv4-Tiny detection network were 97.30% and 96.79% respectively, which were 2.58 percentage points and 2.13 percentage points higher than that of YOLOv4-Tiny target detection network respectively. In terms of the memory occupied by the network structure, I-YOLOv4-Tiny was 1.05 M larger than that of YOLOv4-Tiny, and the detection time was 5.723 ms, which was only 0.078 ms more than that of YOLOv4-Tiny target detection network, which did not affect the real-time detection, However, many indicators have been improved significantly. Compared with I-YOLOv4-Tiny, YOLOv4-Tiny, YOLOv4, SSD-MobileNet and Faster R-CNN target detection networks in different scenes, the average accuracy of I-YOLOv4-Tiny target detection network was the highest, reaching 96.24%, 1.51 percentage points higher than YOLOv4-Tiny, 4.84 percentage points higher than Faster R-CNN, 1.54 percentage points higher than YOLOv4 and 10.74 percentage points higher than SSD-MobileNet. In terms of network structure size, this study was less than one tenth of the size of YOLOv4 network structure, only 24.20 M. In terms of the detection of three blueberries with different maturity, the I-YOLOv4-Tiny target detection network performed best, which could provide accurate positioning guidance for picking robots and early yield estimation. In this study, the target detection network I-YOLOv4-Tiny suffered more interference in the process of blueberry fruit recognition, but the average accuracy of three types blueberry fruits with different maturity was higher than 95%, of which the average accuracy of mature blueberry fruits was the highest. Due to the similar color of immature fruits and background color, the detection accuracy of immature blueberry fruits was relatively poor. It could be seen that the overall performance of the target detection network in this study was the best, which could meet the needs of recognition accuracy and speed at the same time.