根据超声红外热像检测图像的特点，为实现图像中缺陷的识别，提出了一种结合改进的自适应遗传算法和二维最大熵的分割方法，以实现准确、快速地分割出目标缺陷区域。该方法首先对超声红外图像进行预处理，得到了去噪后的图像，然后通过二维最大熵算法选取阈值将图像分为目标区域和背景区域，并结合改进的自适应遗传算法，提高分割速度。实验结果表明，该方法可以有效地滤除图像噪声，相比于穷举法和基于简单遗传算法的二维最大熵分割，本算法具有较好的分割速度和分割精度。

A segmentation method was developed for combining an improved adaptive genetic algorithm with a two-dimensional maximum entropy algorithm based on the image features of ultrasound infrared thermography detection for accurate and rapid segmentation of the target defect region to recognize defects of defect recognition in images. First, the infrared image was processed to obtain a denoised image. Next, the image was divided into the target and background regions using a two-dimensional maximum entropy algorithm; the segmentation speed was improved by combining it with the improved adaptive genetic algorithm. The experimental results showed that this method can effectively filter image noise. Compared with an exhaustive method and the two-dimensional maximum entropy segmentation based on a simple genetic algorithm, the proposed algorithm has better segmentation speed and accuracy.