Image target detection method and device, storage medium and sewage pipeline detection device

Abstract

The invention discloses an image target detection method, and belongs to the technical field of computers. The method comprises the steps of obtaining a to-be-processed target image; inputting the target image into a convolutional neural network model, and extracting at most five feature maps; outputting a prediction box corresponding to each of the at most five feature maps based on the convolutional neural network model; and performing target identification on all the prediction frames through a detection module to obtain the identification result of the target image, wherein the detection module comprises a residual block and a detection convolution layer which are connected in sequence. The invention further discloses an image target detection device, a storage medium and a sewage pipeline detection device. According to the method, accurate image recognition and detection can be performed under the condition of strictly limiting the system memory and the calculation cost.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to an image target detection method, device, storage medium and sewage pipeline detection device. Background technique [0002] With the development of science and technology and the rise of deep learning, the technology of recognizing targets in images has become one of the most important technologies in computer vision, and the application of deep learning in the field of image target detection has made great breakthroughs. A series of Image object learning methods for deep learning algorithms are proposed. For example, deep learning algorithms such as SSD (Single ShotMultiBox Detector). Through these deep learning algorithms, the area where an object is located can be identified from a given image, such as objects such as people, cars, or houses are identified on the image. Therefore, the above algorithm is used in specific fields such as defect monitoring and ris...

AI Technical Summary

Problems solved by technology

However, the current sewage pipeline defect detection model training process requires a large system memory, and there are still problems such as long time consumption, high calculation cost, and very slow detection speed of sewage pipeline images, which cannot be implemented on mobile terminals with small computing resource allocation. , it is difficult to meet the needs of mobile terminals for real-time detection

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