This study presents a computer vision–based system for automatically classifying trailer loading depths and assessing manifest compliance for Roadrunner Transportation using a limited dataset of images. To address data constraints, we employed a transfer learning framework based on DenseNet121 pretrained on ImageNet, enabling effective feature extraction and improved generalization. Input images were standardized and preprocessed prior to model training. We deployed the resulting model through a FastAPI backend with a Gradio-based interface and hosted the system on Railway to ensure scalability and accessibility. The proposed framework evaluated shipping manifests according to configurable business criteria, producing automated pass/fail assessments. Experimental results demonstrated strong predictive performance despite limited training data, underscoring the effectiveness of the approach and the importance of standardized image acquisition. We further discuss recommendations for future work, including structured labeling at the point of data collection and the implementation of continuous feedback mechanisms to enhance model accuracy and generalizability.
Watch the team present this project at 07:45 in the session recording here.
Keywords: computer vision, deep learning, transportation, logistics, image processing, transfer learning, trucking, model deployment
Faculty Advisor
Gizem Agar, PhD is an expert in data analytics, machine learning, and transformation with a passion for mentoring. With 15+ years of interdisciplinary academic and industry experience, her latest work is in manufacturing, pricing, logistics, and supply chain. She has received the CEO Award and Outstanding Achievement in Analytics awards for her contributions to Caterpillar.
Dr. Agar teaches Principles of Data Mining, Python for Analytics, Supply Chain Optimization, Capstone courses and advises students. She holds a PhD and MSc in Industrial Engineering from University of Oklahoma, and BSc in IE and a BSc in CE from Cankaya University, Turkiye. She was a visiting scholar at the Kuhne Logistics University (Hamburg, Germany) and at the Technical University of Vienna (Vienna, Austria).