Abstract
In order to overcome the limitation of manual visual inspection of surface defects of rare-earth magnetic materials and increase production efficiency of traditional rare-earth enterprises, a detection method based on improved SSD (Single Shot Detector) is proposed. The SSD model is improved from two aspects for better performance in the detection of small defects. First of all, the multiscale receptive field module is embedded into the backbone network of the algorithm to improve the feature extraction ability of the model. Secondly, the interlayer feature fusion strategy of bidirectional feature pyramid in PANet (path aggregation network) is integrated into the model. In order to enhance the detection ability of the model, the high-level semantic information is strengthened by an efficient channel attention mechanism. The detection speed of the improved SSD algorithm is 55FPS, and the mAP (mean Average Precision) is up to 83.65%, which is 3.41% higher than of the original SSD algorithm, and the ability to identify small defects is significantly improved.
Highlights
Computer vision technology has very important application and theoretical significance in defect detection of rare-earth magnetic materials
With the development of computer technology and image processing technology, automatic detection based on image processing technology is an inevitable trend. e visual-based surface quality inspection method has very important research value, for example, the steel surface damage detection [1], the railway track defect detection [2], the wafer electron microscope image defect detection [3], and a wide range of applications in other fields [4, 5]
The small target features obtained by the low-level convolutional layer lack semantic information. e nonlinear degree of the features is insufficient, and the features of their scales are not merged, resulting in the loss of part of the detailed information of the model learning. is will not be able to effectively use the contextual information related to the model, which is not conducive to the detection of related targets on the image. ese deficiencies will lead to the failure to identify small-size defects in the defect detection process of rare-earth magnetic materials
Summary
Computer vision technology has very important application and theoretical significance in defect detection of rare-earth magnetic materials. Ey proposed an improved RFB module to increase the size of receptive field for better performance on small object detection [10] These methods have achieved good results, they usually require explicit feature extraction, which leads to unsatisfactory generalization of detection methods. E improved SSD can detect defects on the surface of the magnet more accurately and has a higher recognition rate for small targets. It provides a feasible method for the industrial scene where defect detection is needed. E SSD algorithm directly acts on the output information of the effective feature layer at different scales on the detection layer to generate the bounding box and the confidence of the detection target. E SSD algorithm uses a pyramidal sampling structure to express the semantic information of the image. e shallow
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