AI Vehicle Detection — Analytico Tech Case Study
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🤖 AI / Computer Vision

AI Vehicle Detection for Automotive Monitoring

High-accuracy YOLO-based detection system for open car boots, occluded vehicles, and individual car parts — built for automotive monitoring, smart parking, and security applications.

ClientInternal / Research
IndustryComputer Vision · AI
TypeAI Model / Prototype
GeographyGlobal
StatusIn Development
Overview

The challenge: detecting vehicles in the real world is harder than it looks

Standard object detection models perform well on clean, well-lit, well-framed images. The real world is different — vehicles are partially hidden behind other cars, shot from unusual angles, and mixed into cluttered parking lot scenes. Open car boots, partially visible bumpers, and heavily occluded vehicles all need to be detected reliably.

The objective was to build a production-capable computer vision pipeline that detects open car boots, occluded vehicles, and individual car parts (wheels, doors, bumpers) in real-world images — with accuracy suitable for automotive monitoring, smart parking systems, and security camera feeds.

What We Built

End-to-end computer vision pipeline

1
Dataset Collection & Annotation
Curated and annotated multi-class datasets using Roboflow — covering open boots, closed boots, occluded vehicles, partial car parts, and full vehicles across varied lighting and angles. Applied augmentations (rotation, flip, blur, brightness shift) to simulate real-world conditions.
2
YOLO Model Training on Google Colab
Trained YOLOv8 models on the annotated dataset using Python on Google Colab GPU instances. Ran multiple training rounds with hyperparameter sweeps (learning rate, batch size, IoU threshold) to optimise for mAP (mean average precision) on the validation set.
3
Multi-Class Detection Pipeline
Implemented an OpenCV-based inference pipeline in Python to run detection on static images and video frames. The pipeline outputs bounding boxes, confidence scores, and class labels for: full vehicles, occluded vehicles, open boots, and individual car parts.
4
Occlusion & Edge Case Optimisation
Fine-tuned the model specifically for partial occlusion, varied camera angles, and cluttered multi-vehicle scenes. Evaluated precision, recall, and F1 score per class. Iterated on dataset quality and model confidence thresholds to reduce false positives on car parts.
5
Evaluation & Scalability Testing
Benchmarked model accuracy across 3 test datasets — clean images, occluded scenes, and cluttered parking lots. Documented mAP per class and demonstrated the architecture's scalability for larger annotated datasets and real-time inference deployment.
System Architecture — AI Vehicle Detection Pipeline
Detection pipeline flow
📷
Camera / Image Input
Static · Video · Feed
→
⚙️
Preprocessing
OpenCV · Resize · Norm
→
🧠
YOLOv8 Model
Inference · GPU
→
📊
Detection Output
BBox · Class · Conf.
🚗
Full Vehicle
Class 0
│
🚘
Occluded Vehicle
Class 1
│
🚪
Open Boot
Class 2
│
🔩
Car Parts
Class 3+
MLOps & training stack
1
Data Collection
RoboflowDataset MgmtAnnotationAugmentation
2
Model Training
YOLO v8Google ColabPythonGPU Compute
3
Inference Pipeline
OpenCVPythonBBox RenderingNMS
4
Evaluation
mAPPrecisionRecallF1 ScoreConfusion Matrix
5
Deployment Path
ONNX ExportTensorRTEdge DeviceREST API
Outcomes

What the model achieved

🎯
High detection accuracy on occluded vehicles — model reliably identifies partially hidden cars even in cluttered multi-vehicle scenes, a key differentiator from standard off-the-shelf models.
🚪
Open boot detection — accurately classifies car boots as open or closed across varied lighting and angles, suitable for real-time security and parking applications.
🔩
Individual car part classification — detects wheels, bumpers, and doors as separate classes, enabling granular damage assessment or parts-level monitoring.
📈
Demonstrated scalability — pipeline architecture is dataset-agnostic; adding new vehicle classes requires only additional annotated data and a retraining run, no architectural changes.
⚡
Real-time inference capable — YOLOv8 architecture runs at 30+ FPS on GPU hardware, suitable for CCTV feed analysis and smart parking gate systems.
Challenges & Solutions

What made this technically difficult

Dataset quality and class imbalance — open boot images are rare. Solved with targeted Roboflow augmentation and synthetic samples.
Detection in cluttered scenes — high overlap between bounding boxes. Solved by tuning NMS (Non-Max Suppression) thresholds per class.
Small car part detection — wheels and bumpers are small relative to full frames. Solved with higher-resolution input tiles and mosaic augmentation.
Real-time deployment constraints — inference must run without expensive cloud GPU. Solved by ONNX export path and TensorRT quantization for edge hardware.
Key Technical Decisions
YOLO v8
Fastest single-stage detector with strong mAP/speed tradeoff. Pre-trained on COCO gives strong vehicle feature extraction as starting point. Easier fine-tuning than two-stage detectors like Faster RCNN.
Roboflow
Handles annotation, versioning, augmentation, and dataset export in one platform. Critical for maintaining reproducible experiments and quickly iterating on dataset quality.
Google Colab
Free T4/A100 GPU access removes infrastructure overhead during R&D phase. Model weights are exported and portable to any deployment target after training.
OpenCV pipeline
Lightweight, battle-tested image processing library. No heavy framework dependency for inference — the detection pipeline can run on edge hardware with minimal memory footprint.
Tech Stack
YOLO v8 Python OpenCV Roboflow Google Colab NumPy ONNX TensorRT
Project Metrics
🎯
4 Classes
Vehicle · Occluded · Boot · Parts
⚡
30+ FPS
Inference on GPU hardware
📊
mAP 50+
On validation dataset
🔄
Scalable
Add classes with new data only
Use Cases
Smart Parking Security CCTV Automotive QA Damage Detection Fleet Management

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