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Publication Type : Journal Article
Publisher : IEEE
Source : In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1359-1366). IEEE.
Url : https://ieeexplore.ieee.org/document/9432350
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Verified : No
Year : 2022
Abstract : Background Thresholding is a method used to improvise the training of a model with multiple datasets. The primary objective of this paper is to test the scalability of the Background Thresholding approach incorporated with a proposed object detection model for multiple datasets. The proposed research work has taken the Bosch traffic light dataset for traffic light detection, Tsinghua-Tencent 100K Dataset for Traffic sign detection and Udacity annotated driving dataset for car detection. Different algorithms and deep learning frameworks are experimented to provide a detailed analysis as to which model performs the best for the particular task at hand. The proposed research work is concluded with the Faster-RCNN (Regional-CNN) algorithm, which is coupled with Inception V2 to remain as the best for our approach.
Cite this Research Publication : TR, K.K., Thiruvikkraman, S., Gokul, R., Nirmal, A. and Karthika, R., 2021, May. Evaluating the scalability of a multi-object detector trained with multiple datasets. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1359-1366). IEEE.