Industrial diamond detection method based on improved coyote optimization algorithm and extreme learning machine

To improve the detection efficiency of industrial diamond and ensure product quality, an industrial diamond detection method based on an improved Coyote Optimization Algorithm (COA) and Extreme Learning Machine (ELM) was proposed. The video images of industrial diamond was decomposed into a group of relatively stable and single-dimensional image data according to a certain time series; the deep convolution network Inception-V3 was used to establish a prediction model for multi-perspective 2D image data. On this basis, the prediction results were used as input to construct the ELM model, and the COA improved by reverse learning and Levy flight was used to optimize the input weights and thresholds of ELM to improve the detection accuracy of the industrial diamond model. The detection results of the model were compared with basic ELM, and those of ELM model optimized by Differential Evolution algorithm (DE), Particle Swarm Optimization algorithm (PSO) and basic COA. The comparative experimental results showed that the model had good detection accuracy and generalization ability, which had guiding significance for the qualitative detection of industrial diamond. © 2023 CIMS. All rights reserved.