1. Car Model Recognition with Neural Network CS679 Term Project by Sungwon Jung Computer Science Department KAIST

2. Introduction My initial idea  Several hierarchical classification  Car type -> company -> model.. Project scope is reduced  to only car model recognition  Reasons : lack of data, unreal project scale So, recognition of several pre-selected car model

3. Problem Definition Input  Photo of cars which is parked  Photo from front side and rear side Output  The model name of the car (among pre-selected models)  Don’t consider “unknown” car Pre-selected model in experiment  10 common models in KAIST  Accent, Avante, Espero, Excel, New Sephia, Pride, Sephia, Sonata, Sonata 2, Tico

4. Approach Take a picture Convert to gray scale Edge detectionPart seperation Network construction

5. Data Collection Photo with digital camera  10 pre-selected models  Color image of 756x504 pixels For one car, two pictures were taken  From same direction FrontRear

6. Data Collection  With similar size

7. Edge Detection Canny edge detection method  Find strong and weak edges  Include weak edges only when connected to strong edges  Less likely to be “fooled” by noise First, convert to gray scale. Then detect edge.

8. Part Seperation To reduce complexity, separate by parts  Front side  Engine-cover, Head-light, Radiator-gril, front-overall From Tico From Accent

9. Part Seperation  Rear side  Rear-lamp, center part of rear, rear-overall From Pride From Accent

10. Network Architecture For one part, one network was constructed.  Front  Engine-cover : (input)231x135 -> 47x22, (hidden)522  Head-light : (input)137x68 -> 28x11, (hidden)159  Radiator-gril : (input) 92x54 -> 18x9, (hidden) 86  Front-overall : (input) 236x82 -> 48x13, (hidden) 317  Rear  Rear-lamp : (input) 130x90 -> 26x15, (hidden) 200  Center part of rear : (input) 170x137 -> 34x22, (hidden) 379  Rear-overall : (input) 299x137 -> 61x22, (hidden)676 With these 7 modules, voting was performed

11. Results & Discussion Experimental result  Training with 100 sample data  Evaluation with 90 data  It shows accuracy of about 82% From experiment,  In one model, designs are almost similar for car  So different with handwritten pattern recognition  More tuning of network structure may be helpful Further,  Weighted voting from each module may helpful  For more models, how can we manage them? (not only 10 model)