0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Bridging the Synthetic-to-Real Gap (BSRG): Creating Simulated Datasets for Domain Adaptation to Enhance Vehicle Detection
Authors :
Behnaz Sadeghigol
1
Mohammad Ali Keyvanrad
2
1- Faculty of Electrical & Computer Engineering Malek Ashtar University of Technology
2- Faculty of Electrical & Computer Engineering Malek Ashtar University of Technology
Keywords :
Synthetic dataset،Unreal Engine،Object detection،Domain adaptation،Transfer learning،JLTV dataset
Abstract :
Deep neural network based military vehicle detectors pose particular challenges due to the scarcity of relevant images and limited access to vehicles in this domain. Moreover, Real-world data often poses significant challenges, including privacy, availability, and bias. To mitigate these challenges, synthetic datasets can be leveraged. This article explores the efficacy of synthetic datasets in training state-of-the-art object detection models, specifically focusing on the Joint Light Tactical Vehicle (JLTV). Using the powerful Unreal Engine, which can create highly realistic scenes, we generated a comprehensive synthetic dataset designed to simulate real-world conditions and enhance the training process for various detection algorithms. In this study, we evaluate two distinct models for object recognition: an enhanced domain matching approach utilizing the Masked Image Consistency (MIC) framework and an unsupervised domain matching approach employing confidence-based mixing (ConfMix). The MIC model achieved a mean Average Precision (mAP) at 50% of 47% on real-world data, while the ConfMix model attained a mAP@50 of 55%. These results underscore the pivotal role of synthetic data in advancing object recognition technologies. They also highlight potential research directions for improving synthetic dataset generation and enhancing model performance in practical applications. Examples of this dataset can be accessed at: https://github.com/behnaz-sadeghigol/JLTV_dataset.
Papers List
List of archived papers
Pruning and Mixed Precision Techniques for Accelerating Neural Network
Mahsa Zahedi - Mohammad Sediq Abazari Bozhgani - Abdorreza Savadi
Virus-Antiviral Prediction Using Machine and Deep Learning Methods
Shayan Majidifar - Fatemeh Nasiri - Mohsen Hooshmand
A Smart Electrochemical Biosensor for Arsenic Detection in Water
Keyvan Asefpour Vakilian
Robustness Scan of Digital Circuits Using Convolutional Neural Networks
Mobin Vaziri - Mohammad Mehdi Rahimifar - Hadi Jahanirad
Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network
Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi
Investigation of topological characteristics of Iranian railway network: A network science approach
Sina Firuzbakht - Mohammad Khansari
A New Inter-layer Similarity metric for link prediction in multilayer networks
Alireza Abdollahpouri - Samira Rafiee
A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design
Aliyu Sabo - Theophilus Ebuka Odoh - Samuel Habu - Hossien Shahinzadeh - Farshad Ebrahimi
SAT Based Analogy Evaluation Framework For Persian Word Embeddings
Seyed Ehsan Mahmoudi - Mehrnoush Shamsfard
DFIG-WECS Renewable Integration to the Grid and Stability Improvement through Optimal Damping Controller Design
Theophilus Ebuka Odoh - Aliyu Sabo - Hossien Shahinzadeh - Noor Izzri Abdul Wahab - Farshad Ebrahimi
more
Samin Hamayesh - Version 41.3.1