Traditional machine learning methods(e.g.,hand-coded feature extraction)are sensitive to light sources,equipment installation errors and other factors,which require repeated debugging and experiments and make it difficult to achieve automatic detection in large-scale production.Considering the above-mentioned problems,an in-situ roughness evaluation method is proposed to effectively enhance the efficiency and accuracy of the detection processes.Firstly,an enhanced candidate frame extraction operator for the histogram-of-gradient feature set with low sensitivity parameters is proposed to locate the milling workpiece,and the installation error is corrected using the point matching algorithm.Then,the focusing process of the industrial camera is optimized via the sharpness evaluation metrics.Finally,a lightweight convolutional neural network model for real-time computing at mobile terminals is constructed.The proposed method realizes the classification of surface textures of workpieces with different roughness values,and is experimentally verified on the end milling texture data set.Taking the times of multiplication and addition as the metrics,the performed experiments indicate that the number of floating-point operations(e.g.,add and multiply)required for model inference is reduced by 55%,compared with the general convolutional neural network.In addition,the introduced cost-sensitive loss effectively improves the model's stability to unbalanced data.Compared with the traditional machine learning methods,the accuracy of the proposed model is improved under the same experimental conditions(i.e.,detection frame rate and image resolution),where the recall rate is increased by 21%,and the accuracy rate is enhanced by 8% simultaneously.
维普
万方数据