Truck and Trailer Classification With Deep Learning Based Geometric Features
| Autor: | Aotian Wu, Xiaohui Huang, Jerry Scott, Sanjay Ranka, Anand Rangarajan, Pan He |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Truck
Training set Generalization business.industry Computer science Mechanical Engineering Deep learning Trailer Image processing Hybrid approach Machine learning computer.software_genre Computer Science Applications Automotive Engineering Key (cryptography) Artificial intelligence business computer |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 22:7782-7791 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2020.3009254 |
| Popis: | In this paper, we present a novel and effective approach to truck and trailer classification, which integrates deep learning models and conventional image processing and computer vision techniques. The developed method groups trucks into subcategories by carefully examining the truck classes and identifying key geometric features for discriminating truck and trailer types. We also present three discriminating features that involve shape, texture, and semantic information to identify trailer types. Experimental results demonstrate that the developed hybrid approach can achieve high accuracy with limited training data, where the vanilla deep learning approaches show moderate performance due to over-fitting and poor generalization. Additionally, the models generated are human-understandable. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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