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Noise is usually the important factor that affects the accuracy of low level semantic information extraction in container corner pieces images. Traditional edge detection algorithm usually reduces the physical noise and ambient noise in the image by improving the filter and the threshold, but cannot remove the noise after edge detection. To solve the problem, an algorithm of edge detection based on iterative fitting was proposed. Firstly, container corner pieces image was processed by a series of preprocessing operations to obtain the set of edge points. Secondly, the fitting algorithm was used to process the set of points and the function expression was obtained. Thirdly, the deviation value was defined and calculated to measure the deviation of the target point set to the fitting or detection result. Finally, iteratively removing some points of farthest de-fined distant to fitting result until evaluation functions converge. The result of experiments shows that the proposed algorithm can effectively remove the unreal edge points and better than traditional edge detection algorithm in removing special noise. The proposed algorithm several merits, such as fast convergence rate, higher accuracy rate and good flexibility