Method for detecting dragon fruit based on improved lightweight convolutional neural network

Abstract: The real-time detection of dragon fruit in the natural environment is one of the necessary conditions for dragon fruit automated picking. This paper proposed the lightweight convolutional neural network YOLOv4-LITE. YOLOv4 integrates multiple optimization strategies, and its detection accuracy is 10% higher than traditional YOLOv3. However, the YOLOv4 requires a large amount of memory storage because of the complexity of backbone network and huge calculation, so it is not suitable to be deployed in embedded devices for real-time detection. The Mobilenet-v3 network was selected to replace CSPDarknet-53 as the YOLOv4 backbone network because it can significantly improve the detection speed. Mobilenet-v3 extends the depth of separable convolution and introduces the attention mechanism, which reduces the computation of feature maps and speeds up the propagation speed of feature maps in the network. In order to improve the detection accuracy of small targets, up-sampling is carried out on the 39-layers and 46-layers respectively. The 39-layers feature map is combined with the feature map of the last bottleneck layer, and upsampling is applied twice. The fused feature map uses a 1×1 convolution to enhance the dimension of the feature map. Then, up-sampling is conducted on the 46-layer to fuse with the 11-layer feature map, and the feature map is fused for multi-scale prediction. The convolution is performed three times to obtain a 52×52 scale feature map for the detection of small targets. The 51-layer feature map is combined with the 44-layer feature map and convolution is applied three times, and a 26×26 feature map is obtained for the detection of medium-sized targets. The 59-layer feature map is combined with the 39-layer feature map, and convolution is applied three times, and a 13×13 feature map is obtained for the detection of medium-sized targets. 2513 images of dragon fruit under different occlusion environments were used as data sets for the training experiment. Results showed that the lightweight YOLOv4-LITE network proposed achieved an Average Precision (AP) value of 96.48%, the average of the accuracy and recall rates (F1 score)of 95%, average Intersection over Union (IoU) of 81.09%, and model occupying 2.7 MB of memory. Meanwhile, by comparing and analyzing different backbone networks, the detection speed of Mobilenet-V3 was improved, and 160.32 ms reduced the average detection time compared with CSPDarknet-53. YOLOv4-LITE took only 2.28 ms to detect a 1 200×900 resolution image on the GPU. YOLOv4-LITE network can effectively identify dragon fruit in the natural environment, and has strong robustness. Compared with existing target detection algorithms, the detection speed of YOLOv4-LITE was approximately 9.5 times higher than that of SSD-300 and 14.3 times than that of Faster-RCNN. The influence of multi-scale prediction on model performance was further analyzed, and four feature maps with different scales were used for fusion prediction. The AP value was improved by 0.81% when four scales were used for prediction, but the average time was increased by 10.33 ms, and the model weight was increased by 7.4 MB. The overall results show that the lightweight YOLOv4-LITE proposed in this paper has significant advantages in terms of detection speed, detection accuracy and model size, and can be applied to the detection of dragon fruit in a natural environment.