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The weighing accuracy is an important index of electronic belt scale. It is difficult to achieve accurate measurement due to the influence of environmental temperature and humidity, belt tension and mechanical vibration. The filtering algorithm is introduced into the belt scale data acquisition and processing system. By filtering the collected data can eliminate or reduce the system error and improve the weighing accuracy. By studying the composition structure, weighing principle and error source of belt scale weighing system to analyze and test three filtering algorithms. By comparing the experimental data, an improved adaptive filter algorithm can play a better role and improve the weighing accuracy. It is important to improve the dynamic weighing accuracy of electronic belt scale and reduce the error.

A fire detection model with improved YOLOv4 is proposed for mobile devices with limited computing resources and less accurate object localization. Firstly, the network model of YOLOv4 object detection algorithm is modified, and a depthwise separable convolution network is used instead of traditional convolution in the feature extraction network part to realize the lightweight of fire detection model. Then the Loss function is optimized to solve the problem of inaccurate object detection frame localization. The experimental results show that compared with YOLOv4, the improved algorithm reduces the model parameter by 60.7 % and the detection speed increases by 27.9 %. It is more favorable for the model to be equipped on mobile devices.

Under complex weather conditions, the vehicle vision system has low recognition accuracy for traffic signs, and there are problems such as missed detection and false detection. An improved YOLOX-S traffic sign detection model is proposed. Firstly, a detection layer is added to the YOLOX-S network, so that the model can effectively detect the minimum target and improve the prediction ability of the model. Then the attention mechanism module is added to the YOLOX-S feature fusion network to strengthen the feature extraction function of the network. Finally, the data enhancement mechanism is introduced to the model, so that the detection of the model in severe weather such as haze and rain and snow has strong robustness. The detection model was tested on the Chinese Traffic Sign Detection Benchmark dataset (CCTSDB).

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