5 papers
A Novel Compression Framework for YOLOv8: Achieving Real-Time Aerial Object Detection on Edge Devices via Structured Pruning and Channel-Wise Distillation
Melika Sabaghian, Mohammad Ali Keyvanrad, Seyyedeh Mahila Moghadami
Efficient deployment of deep learning models for aerial object detection on resource-constrained devices requires significant compression without com-promising performance. In this…
Enhancing Small Object Detection with YOLO: A Novel Framework for Improved Accuracy and Efficiency
Mahila Moghadami, Mohammad Ali Keyvanrad, Melika Sabaghian
This paper investigates and develops methods for detecting small objects in large-scale aerial images. Current approaches for detecting small objects in aerial images often involve…
Explaining What Machines See: XAI Strategies in Deep Object Detection Models
FatemehSadat Seyedmomeni, Mohammad Ali Keyvanrad
In recent years, deep learning has achieved unprecedented success in various computer vision tasks, particularly in object detection. However, the black-box nature and high complex…
Architectural Insights into Knowledge Distillation for Object Detection: A Comprehensive Review
Mahdi Golizadeh, Nassibeh Golizadeh, Mohammad Ali Keyvanrad +1
Object detection has achieved remarkable accuracy through deep learning, yet these improvements often come with increased computational cost, limiting deployment on resource-constr…
Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper
Helia Mohamadi, Mohammad Ali Keyvanrad, Mohammad Reza Mohammadi
Domain adaptation, a pivotal branch of transfer learning, aims to enhance the performance of machine learning models when deployed in target domains with distinct data distribution…