Objective

2

Steel plates are important raw materials in industry. In its manufacturing process

a variety of surface defects inevitably arise. These surface defects have a negative effect on the appearance and performance of the product; thus

detecting and controlling them in time is necessary. At present

an increasing number of iron and steel manufacturing enterprises use the machine vision method to detect and identify steel-plate surface defects automatically. The defect detection of the steel-plate surface based on machine vision collects the image of the steel-plate surface by using a charge-coupled device camera. By image denoising and enhancement

the defect image is segmented

the defect features are extracted

and the defect classification is conducted. In image acquisition

being disturbed by the on-site environment of the production line is unavoidable

as are the reflection of the steel plate

the illumination environment or the instability of the optical elements

often resulting in the non-uniform illumination of the image. If the image is not enhanced

great interference in the detection and recognition of small surface defects of the steel plate would occur. The common characteristics of small defects on the steel plate surface are non-uniform gray scale

low contrast between defects and background

obscure edge

diverse and small shape

and a small proportion of a defective area in the entire image

which is even mixed with noise. The contrast between the surface defect of the steel plate and its background is low. To conduct subsequent image analysis and defect recognition effectively

we need to conduct image enhancement processing to emphasize the surface defect information. The purpose of image enhancement is to make the original image clear or emphasize interesting features

thereby improving the overall contrast of the image and enhancing the local details of the image

which has good visual effect and rich information features. On this basis

the surface defect target is segmented from the background by image segmentation; thus

the feature of the defect can be extracted and recognized in the future.

Method

2

Low-contrast image enhancement methods often include histogram equalization (HE)

Retinex model

homomorphic filtering

and gray transform. The HE algorithm is widely used because of its simple principle and easy implementation

but it cannot adapt to the local contrast of the image of small defects on the surface of the steel plate. The Retinex model algorithm is also a common method for low-contrast image enhancement. Based on this model

single-scale Retinex (SSR) and multi-scale Retinex (MSR) algorithms have emerged. These series of algorithms have achieved good results

but the computational complexity is high. Homomorphic filtering can also enhance low-contrast images. This method avoids the distortion of image directly processed by Fourier transform (FFT)

but it also has problems

such as over-enhancement and poor enhancement effect in high-light regions. In low-contrast image enhancement

if the local contrast of the image is enhanced in the spatial domain and the detailed information is enhanced by high-pass processing in frequency domain; the spatial and frequency characteristics of the image are considered at the same time. Compared with Fourier transform

wavelet transform (WT) is a localized analysis of space and frequency. It refines the signal step-by-step by scaling and translation operations

and it finally achieves time subdivision at high frequency and frequency subdivision at low frequency

focusing on arbitrary details of the signal. The wavelet-homomorphic filtering algorithm is used in image enhancement to eliminate non-uniform illumination. First

the image is decomposed by wavelet transform

and then the low-frequency coefficients of wavelet are modified by homomorphic filtering

while the high-pass filtering is applied to the high-frequency coefficients. Then

the low-frequency coefficients and high-frequency coefficients of the processed wavelet are reconstructed to obtain the enhanced image to eliminate non-uniform illumination. After image enhancement with wavelet transform and homomorphic filtering

the surface defect target is segmented to obtain the surface defect area of the steel plate. Many methods are used in image segmentation. The common classical methods are threshold segmentation based on gray histogram and edge detection. Given the low contrast of small defects on the surface of the steel plate

the distribution of gray histogram of the image does not have obvious peaks and valleys; thus

obtaining satisfactory results for image segmentation by using threshold method alone is difficult. The edge detection methods of Roberts

Sobel

and Prewitt operators are also poor for this type of small defects with low contrast

and canny edge detection operator remains widely studied and applied

but the segmentation effect is greatly affected by the threshold. On this basis

this study uses the Otsu-Canny algorithm in defect edge detection. In other words

the method of maximum inter-class variance (Otsu method) is used to determine the adaptive threshold for the Canny operator to perform edge detection.

Result

2

In this study

the algorithm is used to study the multiple types of low-contrast surface small defects on the strip surface

thereby effectively eliminating the non-uniform illumination. The Otsu algorithm or Canny operator cannot easily detect these defects effectively

and the correct detection rate of the Otsu-Canny algorithm in this study is 96%.

Conclusion

2

After image enhancement with wavelet-homomorphic filtering

the Otsu-Canny algorithm is used to detect the edges of small defects with multiple types and low contrast on the surface of the steel plate

and good results are obtained. Image enhancement and image segmentation should focus not only on the effect of processing but also on the real-time performance of the algorithm. In steel-plate surface defect detection based on machine vision

a real-time algorithm can be used for conventional surface defects. This algorithm is suitable for small surface defects with low contrast. To improve the processing speed

the parallel algorithm of a high-performance processor graphic processing unit can greatly improve the speed of image processing

thereby satisfying the effectiveness and real-time performance of the algorithm.