Aiming at the problem of low intelligent detection accuracy of apple fruit, an improved YOLOv8 is proposed based on the original YOLOv8. The multi-head self-attention mechanism(MHSA)is introduced to improve the detection accuracy of the model and verified on the public Apple dataset. Compared with the original YOLOv8, mAP 0.5 increased by 1% and mAP 0.5:0.95 increased by 4.5%. Compared with the popular YOLOv5 and YOLOv7 algorithms, According to the experimental results that the mAP 0.5 obtained by this research algorithm is as high as 95.1 %,and the mAP 0.5:0.95 is as high as 54.3%,which is better than the comparison algorithm. It shows that the improved YOLOv8 has high precision and efficiency of apple positioning,and can serve the apple picking robot for picking.
We propose an improved AlexNet network model, to address the problems of low denoising performance of traditional LeNet-5 neural networks in removing random noise from seismic data. The network retains the original eight-layer calculation depth and uses ReLU as the Activation function to reduce the convolution core and the number of nodes in the convolution layer, thus obtaining higher noise feature extraction accuracy. The network trains the network with 10000 seismic data, tests the network with 1000 data, and optimizes the network. Experiments were conducted using a wide range of Marousi2 seismic data, and the results showed that the proposed network has good denoising performance. Compared with traditional wavelet algorithms, SVD, and LeNet-5 networks, experimental results show that the proposed network can achieve higher PSNR and SNR values and has better seismic data denoising performance compared to the above networks.
Human action recognition is one of the core tasks in the field of computer aided driving. Considering that the auxiliary driving system requires high real-time, and the hardware requirements can not be too high, it is proposed to identify human behavior in a single image. Considering that the night illumination is insufficient, and the infrared camera receives the infrared radiation of the object, it can work at night without the influence of visible light. Therefore, we focus on the human behavior recognition in infrared images. According to the scale of the problem, we first use AlexNet with moderate network depth as the backbone network, then improve the network, modify the classification output layer of the network according to the classification number. After preprocessing the dataset to adapt to improve AlexNet, we trained and tested the network. The experimental results quantify the classification performance of the network. Experimental results show that the proposed algorithm mean average precision, average recall and F1 score are better than traditional methods.
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