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Devanshu Biswas
Devanshu Biswas

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I Built a Dashcam Pothole Detector for Smart India Hackathon — YOLOv8 + GPS + Civic API

#ai #hackathon #beginners #python

🌐 Live demo (LOOK · UNDERSTAND · BUILD): https://dev48v.infy.uk/solve/day2-pothole-detector.html

Day 2 of SolveFromZero — pick a real hackathon problem, ship a working solution. Today's brief is from Smart India Hackathon: Indian roads have ~9 million potholes annually. Municipal complaints rely on citizens manually photographing each one. Build a system that watches dashcam video, detects potholes, GPS-tags them, and posts to the civic complaint API.

This is also the template for any "frame-by-frame civic detection" — garbage dumps, illegal parking, missing signage, encroachment. Same pipeline, swap the model.

The pipeline in 6 stages 

📹 Video stream (30 FPS)
    ↓
🎯 YOLOv8 per-frame object detection
    ↓
✅ Confidence threshold (filter false positives)
    ↓
📍 GPS pairing (lat/lng from parallel GPX track)
    ↓
🔗 Cluster duplicates (same pothole in 30 frames → 1 complaint)
    ↓
📤 POST to civic complaint API
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Each stage is ~20 lines of Python. Total: ~150 lines for the whole thing.

Stage 1+2 — YOLO frame detection

YOLOv8 = state-of-the-art object detector. The n (nano) variant is 14 MB and runs ~30 FPS on a CPU laptop. Roboflow Universe hosts pre-trained pothole-detection weights — no labeling required.

from ultralytics import YOLO
import cv2

model = YOLO("pothole-yolov8n.pt")  # 14 MB pre-trained

cap = cv2.VideoCapture("dashcam.mp4")
while cap.isOpened():
    ok, frame = cap.read()
    if not ok: break

    results = model(frame, conf=0.6)  # confidence threshold
    for box in results[0].boxes:
        x1, y1, x2, y2 = box.xyxy[0]
        conf = float(box.conf)
        cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
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OpenCV reads MP4 files, RTSP streams, or webcams interchangeably. Same code, different source.

Stage 3 — Confidence tuning is the killer detail

Each detection has a confidence score 0-1. The threshold dictates your tradeoff:

Threshold Recall Precision What happens
0.3 100% 60% Reports every shadow / manhole / dark stain
0.6 ~90% ~85% Sweet spot for road damage
0.9 60% 99% High precision but misses smaller potholes

Production deployments typically run at 0.55-0.7 for road-damage detection. Below that = too many citizen complaints filed for shadows. Above = miss the actual problem you're trying to solve.

Stage 4 — GPS pairing

A pothole at lat/lng is 100× more useful than "somewhere in this video." Dashcams usually record GPS either:

  • Embedded in video metadata (GoPro GMD telemetry, Garmin VIRB)
  • Parallel GPX file (most dedicated dashcams)
  • Separate phone log (if you used a phone-based recorder)

Match each video frame's timestamp to the closest GPS sample:

import gpxpy
gpx = gpxpy.parse(open("track.gpx"))
points = [(p.time, p.latitude, p.longitude)
          for trk in gpx.tracks
          for seg in trk.segments
          for p in seg.points]

def gps_at(frame_time):
    # Binary search by time, return (lat, lng)
    return interpolate(points, frame_time)
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Binary search by time, interpolate between the two closest GPS fixes.

Stage 5 — Cluster duplicate detections

You drive past one pothole → it appears in 30 consecutive frames. Without clustering, you'd file 30 complaints for the same hole. Group detections within ~10 m of each other into one complaint, keeping the highest-confidence frame as the evidence.

Cheap approximation: round lat/lng to 4 decimal places (~11 m at the equator). Group by the rounded tuple.

from itertools import groupby

def key(d):
    return (round(d.lat, 4), round(d.lng, 4))

clusters = []
for k, grp in groupby(sorted(detections, key=key), key=key):
    grp_list = list(grp)
    best = max(grp_list, key=lambda d: d.conf)
    clusters.append({
        "lat": best.lat, "lng": best.lng,
        "conf": best.conf, "frame": best.frame
    })
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Production should use proper geo-hashing (Uber's H3 library) for scale. For a hackathon demo, rounding is fine.

Stage 6 — Post to civic API

Most Indian municipal corporations rolled out JSON complaint APIs in 2023-2024 — MCGM (Mumbai), BBMP (Bengaluru), MCG-Pune, GHMC (Hyderabad). Format varies but the shape is universal:

import requests
for cluster in clusters:
    requests.post("https://api.mcgm.gov.in/v1/complaints", json={
        "category": "pothole",
        "lat": cluster["lat"],
        "lng": cluster["lng"],
        "confidence": cluster["conf"],
        "image_url": s3_upload(cluster["frame"]),
        "reporter": "dashcam-auto-v1"
    })
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The municipality dashboard already exists. Your job is the auto-filing pipeline.

Try it now

The 3-tier learning page has a simulated dashcam + live YOLO bounding-box overlay + a feed of complaints filed in real time:

https://dev48v.infy.uk/solve/day2-pothole-detector.html

  • LOOK — start the simulated drive, drag the confidence threshold, watch detections appear or get rejected
  • UNDERSTAND — 9 click-through steps explaining YOLO, OpenCV, threshold tuning, GPS pairing, clustering
  • BUILD — full Python pipeline with real pip install commands

What this unlocks

Same skeleton works for any frame-by-frame civic detection:

Problem Model swap
Pothole detection pothole-yolov8n.pt
Illegal parking Vehicle detection + zone polygon
Garbage dump tracking litter-yolov8n.pt (Roboflow has it)
Red-light running Traffic-light detection + vehicle motion
Sidewalk encroachment Obstruction detection + sidewalk geofence

The video-loop + detect + threshold + GPS + cluster + POST shape is invariant.

What's next in SolveFromZero

Day 3: AI crop disease identifier (Smart India Hackathon). Leaf photo → disease classification + remedy suggestion. Edge AI in the village ag-extension officer's pocket.

🌐 All problems: https://dev48v.infy.uk/solvefromzero.php

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