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.
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.
Aiming at the problem of pedestrian behavior recognition in infrared images, a method based on Improved GoogLeNet is proposed. Firstly, by analyzing the application scenarios and the characteristics of common network models, GoogLeNet with better comprehensive performance is selected as the backbone network. Inspired by NIN, a kind of 1*1 convolution kernel structure is introduced to reduce the number of channels and significantly reduce the number of parameters. Then channel padding and resize to adapt to the network requirements for the training set and test set of the infrared image human behavior data set. Next, the fully connected layer and the classification output layer of the network are modified according to the number of behavior types contained in the data set. The convolution kernel and inception parameter in the pre-training network are introduced to accelerate the network training and improve the generalization ability of the network. Finally, the quantitative index is used to analyze the experimental results and judge the recognition performance of the network. Experimental results shows that the Mean Average Precision, Average Recall and F1 score obtained by the proposed algorithm are better than the traditional methods.
The effective identification of pedestrian dangerous actions at night was a core task of unmanned driving and intelligent assistant driving system. Limited by the network depth and learning ability of traditional convolutional neural network, the performance of the algorithm and its improvement were still unsatisfactory. Considering the imaging characteristics of the camera at night, this paper proposed an infrared pedestrian dangerous action recognition algorithm based on residual network to recognize pedestrian actions at night. Resnet18 network framework was adopted according to the characteristics of infrared images and the scale of problems. In order to adapt to the network input format, the infrared image in the database were preprocessed. The experimental results in the actual infrared pedestrian dangerous action dataset indicated that the mean precision of the proposed method for six types of dangerous actions was improved to 98.3%, and the average recall rate was improved to 98.1%, which was better than the traditional recognition method.
The channel estimation of IR-UWB ultra wideband wireless communication system realized by using compressed sensing theory. Firstly, sparse signal, observation matrix and reconstruction algorithm of compressed sensing theory were discussed. Secondly, discussed the composition of IR-UWB wireless communication system. The IEEE802.15.SG3a channel model was adopted for UWB multipath channel. According to the matrix calculation method of cyclic convolution, the compressed sensing model for channel estimation of IR-UWB system was derived, and GOMP algorithm was used to reconstruct the channel parameters of IR-UWB system. With the help of Matlab software, the simulation results showed that GOMP algorithm can reconstruct the channel parameters of IR-UWB system well.
Pedestrian detection in infrared images has been a hot and difficult research topic in computer version. Traditional methods of pedestrian detection mainly depend on the manual feature for the expression of human body and the results largely relies on the feature representation. Designing artificial features is time-consuming and labor intensive, requires heuristic expertise and experience. Deep learning model based on convolution neural network can automatically learn feature representation from the original images, while avoiding the drawbacks of artificial features. Its difficulty is the choice of network parameters. In this paper, we propose to use deep learning method based on convolution neural network in the process of pedestrian detection. In addition, we analyze the impact of network layers, convolution kernel sizes and feature maps to pedestrian detection in infrared images. The results demonstrate the superiority of our method over traditional methods in detection rate and alarm rate.
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