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• W4200456212 abstract "Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model." @default.
• W4200456212 created "2021-12-31" @default.
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• W4200456212 date "2021-12-18" @default.
• W4200456212 modified "2023-09-23" @default.
• W4200456212 title "Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles" @default.
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• W4200456212 doi "https://doi.org/10.3390/electronics10243159" @default.
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