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multi-Class object Classification using Deep Learning Models in Automotive object Detection Scenarios 16

Multi-Class Object Classification using Deep Learning Models...

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16th International Conference on Machine Vision (ICMV)

作者： Soumya, A. Cenkeramaddi, Linga Reddy Vishnu, Chalavadi Mohan, Krishna C. Indian Inst Technol Dept Comp Sci & Engn Hyderabad India Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway Univ Agder Dept Comp Sci & Engn Grimstad Norway Indian Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781510674639;9781510674622

This paper presents two deep learning models using a multi-perspective convolutional neural network (CNN) for classifying objects in the context of intelligent transportation systems (ITS). The proposed model categorizes objects accurately, enabling them to make well-informed decisions in multi-object (such as Persons, Trucks, Motorbikes, Cars, and Cyclists.) detection in complex scenarios for automotive applications. The custom backbone model is designed based on experimentation with the VGG backbone network based on the VGG backbone network, incorporating a multilayer prediction head and custom feature extraction blocks for classifying multiple objects in complex scenes. The model is to extract abstract features and features at multiple scales with a custom-designed feature extraction backbone with multiple blocks. The proposed models are lightweight and require fewer computational resources for high classification performance. The automotive publicly available dataset with 19800 images and labels has been used. Results show that when we experimented with the VGG backbone CNN model, the classification accuracy of 99.64% is achieved, and on the other hand, the classification accuracy of custom backbone CNN is 99.46%. The performance of the proposed custom model is also compared to those of pre-trained benchmark models. The experimental findings presented in this paper show that the proposed models achieve higher accuracy than the pre-trained models.

关键词： Deep Learning Convolutional Neural Network Image Classification multi-class Classification multi-object prediction Computer Vision

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multi-Target Classification using Deep Learning Models for Automotive Applications 11

Multi-Target Classification using Deep Learning Models for A...

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11th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya, A. Cenkeramaddi, Linga Reddy Vishnu, Chalavadi Lanjewar, Yash Vinod Mohan, C. Krishna Indian Inst Technol Dept Comp Sci & Engn Hyderabad India Univ Agder Dept Informat & Commun Technol Grimstad Norway Indian Inst Technol Dept Architecture & Reg Planning Kharagpur W Bengal India

ISBN: (纸本)9798350315684

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

关键词： Deep Learning Convolutional Neural Network Image Classification multi-target Classification multi-object prediction

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