Deep Learning Target Detection System for Sewage Treatment

Abstract

Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.

Obstacle Avoidance for Automated Guided Vehicles in Real-World Workshops Using the Grid Method and Deep Learning

“…Various methods for acoustic image denoising based on multi resolution tools was proposed firstly, the natural image was divided into blocks at an appropriate rate according to the changes in the sampling matrix, and underwater natural images were measured finally. The neural networks and convolutional neural network algorithms in deep learning were studied by Su et al [18], and an object detection system based on these two algorithms was constructed to test the treated sewage. On the basis of YOLOv3 detection network, the improved network retaining the original basic features of the detection network was optimized and validated by Xiao et al [19] in the context of intestinal endoscopy.…”

Retracted: Deep Learning Target Detection System for Sewage Treatment

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”