POTHOLE DETECTOR (YOLOV4)
On this website you can try the model with your own images and buy it if you are satisfied with its accuracy.
This model expects as input an image of a road, on which it detects potholes.
Number of classes: 1 (pothole)
detections_count = 952, unique_truth_count = 359
class_id = 0, name = Pothole, ap = 76.50% (TP = 258, FP = 68)
for conf_thresh = 0.25, precision = 0.79, recall = 0.72, F1-score = 0.75
for conf_thresh = 0.25, TP = 258, FP = 68, FN = 101, average IoU = 61.88 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.764998, or 76.50 %
Inference time using CPU: 300 ms (on HP Laptop 15-DA0042NH (Processor: Intel(R) Core(TM) i7-8550U CPU))