I’m working on a project where I need to automatically identify which specific car parts got damaged by comparing before and after photos. My goal is to pinpoint exactly what components are affected, like when a front bumper or headlight gets damaged in an accident.
I have around 70 pairs of images showing cars before and after they were damaged. I already tried some basic image processing methods like layering one image over the other to spot differences, but this approach doesn’t work well because the photos aren’t always taken from the exact same angle or position.
I know that Mask R-CNN can help me locate damaged areas in the images, but I’m stuck on the next step. How can I take those detected damage regions and figure out which specific car parts they correspond to? For example, if the model detects damage in a certain area, how do I make it say “this damage is on the headlight” or “this damage is on the bumper”?
Any suggestions on techniques or approaches that could help me bridge this gap between damage detection and part identification would be really helpful.
I’d flip this approach completely based on similar projects I’ve worked on.
Skip the damage detection → part mapping pipeline. Train one model that spots damaged parts directly. Label your data as “damaged_headlight”, “damaged_bumper”, “intact_door” etc.
Your model learns damage patterns and components together. No coordinate mapping headaches.
70 image pairs won’t cut it though. Generate synthetic data - grab clean car photos, add damage overlays to specific spots, then label them. You’ll have thousands of examples in no time.
Here’s what saved me on a factory defect job: anchor part detection to stuff that doesn’t move. Wheels, license plates, door handles stay in roughly the same spots no matter the photo angle. Use these as reference points to normalize your part boundaries.
But honestly? Your biggest gains come from preprocessing. Auto-correct perspective using feature matching between before/after shots. SIFT or ORB descriptors handle this well.
Once your images align properly, even basic bounding box overlap calculations work reliably for part ID.
Template matching works way better for alignment than most people realize. I had the same headache on an insurance project where photo angles were all over the place. Here’s what worked: build reference templates for each car part using fixed landmarks - grilles, wheel wells, stuff that’s always there. Map your damage coordinates to these reference points instead of trying to make photos line up perfectly. I used a hierarchical approach that made everything click. Find the car’s orientation first, grab the major structural bits, then break it down into smaller zones. Your Mask R-CNN gets way more accurate when you can limit where it’s looking - like telling it ‘only search for door damage in the door region.’ Don’t try normalizing photos after the fact. Train your part detection model on crazy angles from day one. Pro tip: automotive repair manuals are goldmines for ground truth part boundaries. They show exact component layouts that work great with real photos once you factor in perspective.
you need semantic segmentation plus object detection. train a model to recognize car parts first, then use IoU to match damage areas with part boundaries. opencv’s got solid tools for spatial mapping, but your dataset’s probably too small.
Multi-class detection with transfer learning is probably your best approach. Don’t separate damage detection and part identification - just combine them into one classification problem. I built a similar automotive inspection system last year. We fine-tuned a pre-trained ResNet to classify image patches as “damaged_front_bumper”, “scratched_door_panel”, “cracked_headlight”, etc. Way simpler than trying to match coordinates between two separate models. Here’s what worked: use sliding windows on your difference images. Pull patches from areas where pixels changed significantly between before/after shots, then classify each patch directly. This sidesteps your alignment issues since you’re working with small regions instead of full images. With a small dataset, go heavy on data augmentation. Random rotations, brightness changes, and synthetic damage overlays really expanded our training set. Also try using car technical diagrams or parts catalogs as extra training data - the clean part boundaries help classification accuracy. Practical tip: start with major components first. Bumpers, doors, and lights are much easier to distinguish than smaller stuff like trim pieces or mirrors.
You need a two-stage setup: one model detects damage, another maps it to car parts.
Start with a parts database. Segment your car images into labeled regions - bumper, headlight, door, whatever. Train a model to spot these parts in any photo, no matter the angle.
Run both models, then overlay the results. When Mask R-CNN finds damage coordinates, check which part sits in that spot. Basic coordinate matching works.
The tricky part? Different car models and angles. You’ll need data augmentation and geometric transformation to normalize perspectives.
Managing this pipeline manually sucks though. You’re juggling model orchestration, data preprocessing, coordinate mapping, and result aggregation. Automation saves your sanity here.
I’ve built similar computer vision workflows. The game changer was automated pipelines that handle everything end-to-end. Chain your image preprocessing, run multiple ML models in sequence, handle coordinate mapping, even retrain on new data.
Just feed it image pairs and get structured results back - zero manual work. Makes everything scalable and maintainable.
Check out Latenode for building these automated ML workflows: https://latenode.com