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.
Industrial wastewater classification is the basic work of water pollution prevention and control and water resources management, water chemical oxygen demand is the core indicator to measure the quality of water, compared with domestic sewage detection, the research on industrial wastewater classification is relatively lagging behind. Aiming at the shortcomings of COD classification model of industrial wastewater using convolutional neural network or long shortterm memory network alone, this paper constructs a hybrid model based on CNN and LSTM. The COD data of industrial wastewater were measured by ultraviolet-visible spectroscopy, abstract features were extracted from the data using CNN, and finally entered into LSTM, and the results of classification of industrial wastewater according to COD concentration were obtained, and the classification accuracy of the model reached 98.66%, which was 3.36% and 4.7% higher than that of CNN and LSTM alone.
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