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Autommatic extraction of Brinell indentation contour is a key step to improve the efficiency of Brinell hardness detection.In order to solve the shortcomings of extracting Brinell indentation contour by traditional machine vision algorithm, an automatic detection of brinell hardness indentation diameter is realized by FasterRCNN model. According to the characteristics of Brinell hardness indentation circle detection, an improved FasterRCNN model is proposed.The variance of length and width of the predicted detection frame is added into the frame regression loss function in the Classification network, and the optimization objective of the improved frame regression function is modified whose goal is to minimize both the gap between the real detection frame and the predicted detection frame and the gap between the width and height of the predicted detection frame. The brinell hardness test based on the improved FasterRCNN model can provide more accurate target prediction detection frame and achieve more accurate detection effect.At the same time, the data enhancement method is introduced to expand the effective data size. The result shows that the Brinell hardness model detection method based on FasterRCNN is suitable for corroded and smooth metal surfaces.Also,the accuracy of the improved FasterRCNN network model is 97.08%, which is 0.73% higher than the original model, and the normalized mean square error (nMSE) is 0.001226, which is 40.31% lower than the original model.The effect on improvement is obvious,and make up for the difficiency of particle swarm dynamic contour model (Snake model).