Illegal dog walking identification method

Abstract

The illegal dog walking identification method comprises the following steps: 1) collecting images of high-altitude cameras in a large number of streets and other dog sample data sets, calibrating thedata sets according to on-site management requirements, and determining a used one-stage target detection algorithm model; 2) constructing a parameter self-adaptive loss function; and 3) constructinga loss function LOSS of a one-stage target detection algorithm model; 4) updating the weight of the one-stage target detection algorithm model by adopting a gradient descent method until the model converges; and completing detection of a dog sample in an actual system by using the trained model, and giving an alarm to complete detection of illegal dog walking in a street. The method has the advantages that the parameter adaptability of the target detection model can be improved, and the accuracy of target detection is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition and computer vision, and relates to an illegal dog walking recognition method. Background technique [0002] At present, individual residents in the street do not follow the property regulations to walk their dogs illegally, which has caused unsafe problems for other owners who go out for exercise and walks. The traditional method is mainly through the uninterrupted inspection of the streets by the property administrators, and dissuades the illegal dog owners after they are found. However, this method requires a lot of manpower and material resources to implement, and it cannot achieve real-time and all-round supervision of the entire street. The use of high-altitude security cameras in the existing streets to detect illegal dog-walking behaviors can not only achieve real-time monitoring, but also save manpower and material costs, and equipment maintenance and repair are also very easy....

AI Technical Summary

Problems solved by technology

Although the two-stage target detection model has better detection accuracy, its forward reasoning speed is slow and cannot meet the real-time requirements of business scenarios

In the traditional one-stage target detection algorithm model, the real-time performance of the algorithm is better, but it cannot reach the detection accuracy of the two-stage target detection algorithm model

There are a large number of street background objects in the image detection target. Although the loss value of the street background object is small, the number is far more than the target belonging to the dog sample. It is difficult for the traditional target detection method to obtain a high value in this complex scene. recognition accuracy, so there is an urgent need for a highly adaptive target detection method

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