Optimized Convolutional Forest by Particle Swarm Optimizer for Pothole Detection

Poor road maintenance leads to potholes on the road. Potholes are responsible for road accidents and even deaths in developed and developing countries. Detecting and filling road potholes is an essential part of road maintenance. Sustaining a reliable and safe road for communication depends on pothole detection. This study presents a novel combination of a convolutional neural network and an optimized machine-learning model by a heuristic algorithm for pothole detection. The proposed method comprises a shallow convolutional neural network for feature extraction and an optimized random forest model for pothole detection. The proposed model initially uses the shallow convolutional layer to extract feature sets from input pictures. Then, the particle swarm optimizer is used to eliminate irrelevant features. Finally, a combination of random forest and a particle swarm optimizer is used for pothole detection. Particle swarm optimization indicates the best subset of the extracted feature set for final pothole detection. We added 171 pictures to the already available 665 pothole pictures to evaluate the proposed method. The test set was isolated from the training set, and we trained the model on k-fold cross-validation. The experimental result indicates 99.37% accuracy, 99.37% precision, 99.38% sensitivity, and 99.38% F1-score for discriminating potholes from roads without potholes by proposed methods. The response time of the proposed method for pothole detection is 0.02 s. The proposed method can be utilized for real-time pothole detection.