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Pothole Identification and Dimension Approximation with YOLO Darknet CNN

The automation of daily routine tasks is day-by-day increasing. This is a necessary scenario, especially in today's lifestyle. One such automation is driverless vehicle driving which requires the prompt identification of the potholes present on the roads. A lot of research is present to deal with the real-time pothole identification challenges. However, none of the existing research is capable of achieving prompt accurate prediction. To contribute to the existing literature, this paper proposes a Dark Convolutional Neural Network (Darknet CNN) based model called tiny YOLO (You Only Look Once) for pothole identification and dimension approximation of the potholes in order to alert the driver (or automation system) for the safe driving. The proposed model is implemented for the number of epochs to check the performance of the model. The performance metric used for the model evaluations includes Precision (P), Recall (R), Average Precision (AP), and micro Average Precision (mAP) as evaluation criteria with low GPU memory utilization, bounding box area, object Area, and total area. The model gives the best performance for the 270 epochs. The proposed model is light weighted, which helps the video to store in minimum space and lesser time span.