Abstract
Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.
Highlights
Monitoring farmland weeds and taking timely control measures are conducive to the growth of crops and the increase in yield
The semantic segmentation results are more abundant in contour details than the original model BiSeNetV2, which are closer to the labeled image
We proposed two lightweight models suitable for low-altitude remote sensing rice weed recognition combining with deep learning semantic segmentation technology and embedded hardware to realize the real-time recognition of rice weeds on the airborne end
Summary
Monitoring farmland weeds and taking timely control measures are conducive to the growth of crops and the increase in yield. Deep learning technology is increasingly applied to the recognition of agricultural scenes by UAVs [14,15,16], especially in the weeds recognition of offline UAV low-altitude remote sensing images, which has achieved remarkable results. In the entire feature restoration process, the convolution output of each stage in the feature fusion branch participates in the booster training strategy (as shown in the yellow dashed box in Figure 7), that is, through the upsampling operation of the Seg Head module, the feature maps with the same dimension as the prediction results are output to supervise the overall training process of the network, thereby increasing the feature representation in the training phase. Since the Seg Head module only participates in the training stage of the network and can be completely discarded in the inference stage, it has less impact on the inference and calculation speed of the network
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