Deep learning-based image recognition for autonomous driving

By Hironobu Fujiyoshi, Tsubasa Hirakawa, Takayoshi Yamashita. IATSS Research, 2019

390 citations recorded by the source corpus.

Abstract Various image recognition tasks were handled in the image recognition field prior to 2010 by combining image local features manually designed by researchers (called handcrafted features) and machine learning method. After entering the 2010, However, many image recognition methods that use deep learning have been proposed. The image recognition methods using deep learning are far superior to the methods used prior to the appearance of deep learning in general object recognition competitions. Hence, this paper will explain how deep learning is applied to the field of image recognition, and will also explain the latest trends of deep learning-based autonomous driving.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/460bcc9b52c17ff95b2dc0de831ca6780a33ceaa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sheng Jin
2. Medhi Bennis
3. Deniz Gündüz
4. Shijian Lu
5. Jingyi Zhang
6. Jiaxing Huang

Citations in the corpus

• Vision-Language Models for Vision Tasks: A Survey (2023, background): “, image classification, object detection and semantic segmentation) is a long-standing challenge in computer vision research, and it is also the cornerstone of a myriad of computer vision applications in autonomous driving [1], [2], remote sensing [3], [4], robotics [5], [6], etc.”
• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Nowadays, ML is used in facilitating several management aspects in agriculture [12] and in a plethora of other applications, such as image recognition [43], speech recognition [44], autonomous driving [45], credit card fraud detection [46], stock market forecasting [47], fluid mechanics [48], email,”
• Federated Learning in Vehicular Networks (2020, background): “Deep neural networks that are trained in an end-to-end manner on a huge amount of RADAR/LIDAR/camera sensor data, such as convolutional neural networks (CNNs), have emerged in recent years as a better alternative [4].”
• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “[45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], scene understanding [68], [69], [70], tracking [71], [72],”