Optimized Monitoring of Underwater Marine Waste Using Knowledge Distillation
Year: 2024
Type: conference-paper
Abstract: In this study, we present a method using knowledge distillation to create faster versions of deep architectures with fewer parameters for underwater marine waste classification. The proposed architectures, derived from VGG19, ResNet50, and DenseNet121, can independently classify images for remote monitoring with lower latency and improved performance. All deep models were trained on the JAMSTEC library of deep-sea images and compared with the original dense architectures that have higher parameter counts. The proposed models, with approximately 25% of the original deep architecture parameters, showed improved performance in terms of accuracy and inference time, achieving 70% accuracy through knowledge distillation.
Authors: B. Jalil, L. Maggiani, L. Valcarenghi
Institution: Scuola Superiore Sant'Anna
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
OpenAlex ID: W4404954650