In order to solve the problems of low localization rate and poor stability of laser ranging system, a system based on the TDC-GP22 timing unit and the pulsed laser ranging method in the time-of-flight (TOF) method is designed to realize laser ranging. The domestic AL0905P75WT18-03FW laser diode and power MOS tube are used as the laser transmitter module to realize the high stability of laser emission, and the domestic AA-S0905 L0500 LCC3-2-B avalanche diode is used as the laser detector to convert the optical signal into current signal, which is further processed by amplification, filtering and so on and then passed through the TDC-GP22 to realize the laser distance measurement. After further processing, the light signal is converted into current signal through amplification, filtering and other further processing, through TDC-GP22 timing, thus realizing laser distance measurement. The system adopts domestic light source and photodetector, domestic voltage regulator chip, operational amplifier and FPGA chip, which improves the reliability, stability and autonomy of the system.
With the rapid development of information technology, traditional neural networks used as feature extraction networks can improve the network’s fitting ability but may lose information for small object detection, resulting in low accuracy. In this paper, the image acquisition device and Unet detection model were built independently. The algorithm accurately detects the sensor chip overflow using image processing techniques with OpenCV. Finally, the detected images are presented using PyQt.Experimental results show that the improved Unet-glue algorithm achieves better segmentation accuracy for chip overflow. It also demonstrates strong robustness and practicality in the field of small object defect detection.
One of the research directions of target detection-based computer vision, in which small target detection is the key and difficult research direction in target detection. Traditional target detection algorithms include Faster RCNN, YOLO, SSD, etc., and there is a problem that indicators such as detection accuracy, false detection rate, and missed detection rate are not ideal for small target detection tasks. In order to improve the above problems, this paper proposes an improved target detection algorithm based on YOLOv5. First, the CBAM attention mechanism is introduced in the Backbone part to strengthen the important feature channels; then a detection layer is added to the network according to the characteristics of the data set to strengthen the extraction ability. Experiments show that the improved YOLOv5s_CS algorithm has a mAP value of 75.1% on the test set, which is 3.9% higher than the original YOLOv5s network.
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