It is difficult to gather the high-definition and readable images of insulators in bad conditions such as foggy day and dim light. The available contamination detection methods of visible images does not have generality due to the difficulty of extracting insulator objects and color features of surface region. Therefore

this paper proposes a image enhancement based detection method of non-soluble deposit density(NSDD)levels of porcelain insulators. Firstly

the improved multiple scale Retinex with color restoration(MSRCR)algorithm is used to enhance the clarity and contrast of the collected insulator images. Secondly

the two-dimensional minimum error algorithm and the morphological filter algorithm are combined to segment and extract the surface region of insulators. And seven characteristic values in six channels are extracted

such as mean value

maximum value

minimum value. Then the Smean

Smax and Svar with highly classification ability are selected as the identify features of NSDD levels by using the Fisher criterion function. Finally

the back propagation(BP)neural network optimized by the mind evolutionary algorithm(MEA)is used for simulation and forecast. The experiment results show that the recognition accuracy rates of the probabilistic neural network algorithm and the BP neural network optimized by particle swarm optimization(PSO)algorithm are 88. 00% and 93. 00%

respectively. In comparison

the accuracy rate of the proposed method is 95. 00%

which shows that it can accurately identify the NSDD levels of insulators in bad conditions.