Introduction
Weighted least-squares model and implementation details
Preprocessing
Weighted least-squares model
Fig.3 Detection results of different methods. (a) is the input image. (b) and (d) are the results of the common least-squares model. (b) is the enhanced residual image, and (d) is the defect segmentation result. (c) and (e) are the results of the weighted least-squares model. (c) is the enhanced residual image, and (e) is the defect segmentation result |
Defect segmentation
Experiment results and discussion
Tab.1 Details of the test samples used in the experiments |
sample | number of defect-free buttons | number of defective buttons | ROI size/pixel |
---|---|---|---|
sample1 | 51 | 104 | 233×233×3 |
sample2 | 60 | 39 | 199×199×3 |
sample3 | 151 | 189 | 167×167×3 |
Tab.2 Experimental performance of different methods |
sample | saliency-based method | ICA-based method | proposed method | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
TPR | TNR | R | TPR | TNR | R | TPR | TNR | R | |||
sample1 | 0.92 | 0.72 | 0.78 | 0.78 | 0.91 | 0.87 | 0.94 | 0.94 | 0.94 | ||
sample2 | 0.98 | 0.51 | 0.79 | 0.77 | 0.87 | 0.81 | 0.98 | 1 | 0.99 | ||
sample3 | 0.95 | 0.71 | 0.82 | 0.75 | 0.86 | 0.81 | 0.97 | 0.99 | 0.98 |
Comparison of detection results
Fig.4 Comparison of defect detection results on three samples by using different methods. (a)–(c) correspond to samples1–3. The first column contains the original images; the second and third columns show the results of saliency-based and ICA-based methods, respectively; and the last column presents the detection results of the proposed method |
Tuning of training dataset size
Effect of weight matrix on recognition rate
Tab.3 Relationship between recognition rate and weight matrix W |
recognition rate | |||
---|---|---|---|
sample1 | sample2 | sample3 | |
0.94 | 0.99 | 0.98 | |
0.88 | 0.93 | 0.95 | |
0.75 | 0.78 | 0.89 |
Effect of parameter setting
Receiver operating characteristic (ROC) analysis of control constant C
Analysis of Gaussian components K
Tab.4 Recognition rate versus number of Gaussian components K |
Gaussian components K | recognition rate | ||
---|---|---|---|
sample1 | sample2 | sample3 | |
1 | 0.61 | 0.62 | 0.66 |
2 | 0.82 | 0.88 | 0.81 |
3 | 0.90 | 0.98 | 0.98 |
4 | 0.94 | 0.99 | 0.98 |
5 | 0.94 | 0.99 | 0.98 |
Effect of rotation deviation
Effect of illumination variation
Fig.8 Effect of illumination variations. (a) Original inspection images under different illumination intensities (top to bottom correspond to 2500, 3000, 3500, and 4000 lx); (b) adaptive template images corresponding to the first column; and (c) binary results corresponding to the first column |
Tab.5 Recognition rates under different illuminations |
illumination/lux | 2500 | 3000 | 3500 | 4000 | 4500 |
---|---|---|---|---|---|
recognition rates | 0.93 | 0.96 | 0.98 | 0.97 | 0.93 |