基于压力容器裂纹图像检测及识别算法研究

压力容器作为一种特种设备，其安全越来越受到重视。为了保障其安全运行，选择压力容器裂纹图像为研究对象，构建检测及识别算法模型。针对算法模型在实际部署时受到内存空间、处理器计算能力等多方面硬件条件的制约问题，提出了基于NewEfficientNet-B0的轻量化方法，结果表明算法模型降低模型参数数量达78%。针对微小裂纹图像识别难度较大问题，提出了改进多尺度预测的方法，测试数据集上达到了81%的检测识别准确率。

Image detection and identification algorithm of pressure vessel cracks

As a kind of special equipment, the safety of pressure vessels attracts more and more attention. To ensure their safe operation, using the pressure vessel crack image as the research object, this paper constructed an algorithm model for the crack detection and identification. Generally, the algorithm model is constrained by various hardware conditions, such as memory space and processor computing power during actual deployment. Therefore, a lightweight method based on NewEfficientNet-B0 was proposed. The results show that the algorithm model reduces the number of model parameters by 78%. To deal with the difficulty of recognizing tiny crack images, an improved multi-scale prediction method was proposed. The detection and recognition accuracy rate of 81% was achieved on the test data set.