{'fraction_non_alphanumeric': 0.021058315334773217, 'fraction_numerical': 0.00755939524838013, 'mean_word_length': 5.156146179401993, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automatic pothole classification and segmentation using android smartphone sensors and camera images with machine learning techniques

In most developing countries, a lag in maintaining the existing road infrastructure often leads to development of potholes, cracks, etc. Such irregularities in maintenance are one of the main causes of road accidents as well as wear and tear of vehicles. This necessitates development of automated techniques for detecting and segmenting the potholes on roads. In this paper, a solution is proposed to detect and segment dry and wet potholes (potholes filled with water) using smartphone sensors and camera images. For the first set of experiments using smartphone sensor records, various machine learning techniques (RF, XGBoost and ANN) with balanced and imbalanced classifiers were used to detect the potholes. A new pothole detection approach with the sensor data on two-class (pothole and normal) classification problem is proposed using hypothesis testing. An optimal threshold value is empirically obtained using GMM (Gaussian mixture model) to classify the data points into one of the two classes that rendered 70% accuracy with precision and recall values ranging from 50% to 60%. For the second set of experiments on camera images and videos captured from a moving vehicle, popular semantic segmentation techniques like Mask-RCNN and U-Net algorithms were applied. The results showed that Mask RCNN rendered an overall accuracy of 80% with precision, recall and F1 score of 0.85. However, U-Net provided a lower overall accuracy of 31% and low recall values when a large number of potholes were present in the image. The proposed framework is not only a cost effective mechanism for detecting potholes on existing roads, its usage can also be extended for real time pothole detection in autonomous vehicles.