Small target detection method based on improved Faster R-CNN

Abstract

The invention discloses a small target identification technology based on an improved Faster R-CNN (Recurrent Convolutional Neural Network). The method has certain universality in the small target detection direction, and cloth defect detection is taken as an explanation case. A large number of small target defects and extremely large length-width ratio defects exist in cloth defects. For a smalltarget problem, feature pyramid fused multi-scale detection is added into a Faster R-CNN, and a multi-scale detection algorithm has certain universality and transportability for small target detection; for the problem of an extreme length-width ratio, preliminary statistics need to be carried out on the length-width ratio and the area of an actual cloth defect data set, then clustering is carriedout in an algorithm framework, and the size of a Faster R-CNN anchor box is reset through a K-means + + method. Based on the improved Faster RCNN algorithm model, cloth defects can be accurately identified, and a good identification effect can also be achieved for small target defects and defects with an extremely large length-width ratio.

Description

technical field [0001] The invention relates to the field of target detection in deep learning, and is aimed at small target detection, especially cloth defect detection technology. Background technique [0002] During the production process, various conditions such as equipment failure, factory ambient temperature changes, and staff operating errors will affect the quality of cloth production. Cloth defect is a key factor affecting the quality of fabric production, and directly affects the fabric quality grade, so the detection of cloth defect is particularly important. [0003] In the traditional cloth defect detection process, the cloth defects are mainly located, detected and marked by human eyes. Obviously, this way of working is inefficient and easily affected by the subjective experience and emotions of cloth inspection workers, and false detections and missed detections often occur. In addition, the detection of cloth defects is labor-intensive for workers, which n...

AI Technical Summary

Problems solved by technology

Obviously, this way of working is inefficient and is easily affected by the subjective experience and emotions of the cloth inspectors, and false detection and missed detection often occur

In addition, the detection of cloth defects is labor-intensive for workers, which not only damages the eyesight of cloth inspection workers, but also easily causes pneumoconiosis

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