In order to obtain better image edge information

an edge detection algori山m combining BP (Back Propagation) neural nehvork and morphology is proposed. The Sigmoid function is commonly used as the excitation function in BP neural networks

but the traditional Sigmoid function is single in form and lacks flexib山ty. Therefore

it is very important to provide an adjustable Sigmoid function. First

a fully smooth Sigmoid function construction method is given

which is used as the excitation function in the BP neural nehvork to detect the edge of the image effectively. Then

using the idea of multi-scale and multi-structure

an improved morphological edge detection algorithm is proposed

and the edge image w心small noise and continuous is obtained by applying小is algorithm. Finally

the wavelet analysis is used to fuse the BP neural nehvork and the improved morphological algoritl1m

and 小en an edge detection fusion algorithm is obtained. The simulation results show that 如e evaluation index of the fusion algorithm is better Ulan a single edge detection algorithm and the detected image edge lines are complete and cle釭