Image processing equipment

The embodiment of the invention relates to the technical field of target detection, in particular to image processing equipment which calculates position deviation between a bounding box position anda wrinkle position according to the bounding box position, the wrinkle position, the minimum closure area and the union area, so that the overlap ratio of the boundary frame position and the wrinkle position can be better reflected. On the basis, the position deviation is used for calculating the error between the first label and the detection result, and the model parameters of the preset convolutional neural network are reversely adjusted according to the error, so that the model parameters can be better optimized, and the obtained wrinkle detection model is more accurate. 本发明实施例涉及目标检测技术领域，尤其涉及图像处理设备，图像处理设备根据所述边界框位置、所述皱纹位置、所述最小闭包面积以及所述并集面积，来计算所述边界框位置与所述皱纹位置之间的位置偏差，能更好的反映所述边界框位置和所述皱纹位置的重合度。在此基础上，将所述位置偏差用于计算所述第一标签和所述检测结果之间的误差，并根据所述误差反向调整所述预设卷积神经网络的模型参数，可以使得所述模型参数得到更好的优化，从而，得到的皱纹检测模型更加准确。