Automated quantification of small defects in ultrasonic phased array imaging using AWGA-gcForest algorithm

The imaging quality of ultrasonic phased array technology significantly affects the quantification of small defects inside metals. Moreover, traditional manual evaluation methods require a substantial amount of manpower and time, and they are easily influenced by subjective factors. To mitigate the impact of imaging quality on the automated quantification of defects, this study proposes an improved gcForest algorithm (AWGA-gcForest, Adaptive window genetic algorithm- gcForest). Firstly, the acquired ultrasonic full matrix data from the phased array is processed using full focusing A-scan technique. Additionally, a window averaging weighting method is introduced for ultrasound signal processing to eliminate the interference from incident waves and backwall echoes. Subsequently, a multi-scale adaptive sliding window adjustment strategy based on Mean Absolute Deviation (MAD) is employed to reduce the computational time complexity of the gcForest algorithm. Furthermore, a weighted approach optimised with a genetic algorithm is utilised in the cascade forest to improve the classification accuracy and generalisation capability. Experimental results demonstrate that the proposed method achieves an accuracy of 97.50%, precision of 97.26%, recall rate of 96.63%, and F1 score of 96.92, exhibiting higher detection accuracy compared to other models while also reducing time costs.