Modern deep learning models require large amounts of data to train, and the acquisition of data can be challenging. Synthetic data provides an alternative to manually collecting real data, alleviating problems associated with real data acquisition. For recycling processes, classifying metal scrap piles containing hazardous objects is important, where hazardous objects can be damaging and costly if handled incorrectly. Automatically detecting hazardous objects in metal scrap piles using image classification models requires large amounts of data, and metal scrap piles contain large variations in objects, textures, and lighting. Furthermore, data acquisition can be challenging in the recycling domain, where positive objects can be scarce and manual acquisition setup can be challenging. In this thesis, synthetic images of metal scrap piles in a recycling process are created, intended for training image classification models to detect metal scrap piles containing fire extinguishers or hydraulic cylinders. Synthetic images are created with physically based rendering and domain randomization, rendered with either rasterization or ray tracing engines. Ablation studies are conducted to investigate the effect of using domain randomization. The performance of models trained on purely synthetic datasets is compared to models trained on datasets containing only real images. Furthermore, photorealistic rendering with ray tracing rendering is evaluated by comparing F1 scores between models trained on data sets created with rasterization or ray tracing. The F1 scores show that models trained on purely synthetic data outperform those trained solely on real data when classifying images containing fire extinguishers or hydraulic cylinders. Ablation studies show that domain randomization of textures is beneficial both for the classification of fire extinguishers and for the classification of hydraulic cylinders in metal scrap piles. High dynamic range image lighting randomization does not provide benefits when classifying metal scrap piles containing fire extinguishers, suggesting that other lighting randomization techniques may be more effective. The F1 scores show that synthetically created images using rasterization perform better when classifying metal scrap piles containing fire extinguishers. However, when classifying metal scrap piles containing hydraulic cylinders, images created with ray tracing achieve higher F1 scores. This thesis highlights the potential of synthetic data as an alternative to manually acquiring real data, particularly in domains where data collection is challenging and time-consuming. The results show the effectiveness of domain randomization and physically based rendering techniques in creating realistic and diverse synthetic datasets.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-204934 |
| Date | January 2024 |
| Creators | Pedersen, Stian Lockhart |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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