Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review

Alaba, Simegnew; Ball, John

doi:10.1109/jsen.2023.3235830

Cited by 44 publications

(17 citation statements)

References 121 publications

(175 reference statements)

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“…The growing interest in autonomous vehicles, as evidenced by large investments made by the largest automotive industry, 35 as well as the development of machines and applications that can interact with people, is playing an essential role in the advancement of vision-based object detection 10,36,37 in the road environment. With the development of DL algorithms, many breakthroughs have been made in detection algorithms.…”

Section: Object Detectionmentioning

confidence: 99%

“…DL has been particularly successful in object detection tasks, where it has outperformed traditional ML techniques. 9,10 DL is a powerful tool allowing the development of algorithms for object detection and decision-making based on large amounts of data 11 . 12 The AV needs to interpret sensory information accurately and make real-time decisions based on the data collected by different sensors.…”

Section: Introductionmentioning

confidence: 99%

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Active learning-based pedestrian and road sign detection

Islam,

Nabi,

Rahman

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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For autonomous driving, pedestrian and road signs detection are key elements. There is much existing literature available addressing this issue successfully. However, the autonomous system requires a large and diverse set of training samples and labeling in real-world environments. Manual annotation of these samples is somewhat challenging and time-consuming. In this paper, our goal is to get better detection accuracy with minimal training data. For this, we have employed the active learning algorithm. Active learning is a useful method that selects only the effective portion of the dataset for training and reduces annotation costs. Though it uses only a small amount of the training data, it provides a high detection accuracy. In this work, we have chosen the deep active learning model for object detection via the probabilistic model of Choi et al. and modified the depth scale of different layers in the backbone. As real-world data may contain noise, motion, or other disruptions, we modified the original model to obtain improved detection results. In this experiment, we create a customized dataset that contains pedestrians, road signs, traffic lights, and zebra (or pedestrian) crossings to deploy the active learning algorithm. The experimental results show that the active learning model can produce good detection outcomes by accurately detecting and classifying pedestrians, road signs, traffic light, and zebra (or pedestrian) crossings.

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Section: Object Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active learning-based pedestrian and road sign detection

Islam,

Nabi,

Rahman

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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show abstract

“…In computer vision, deep learning has been widely used to solve a variety of issues, including detection, localization, estimation, and classification. [10][11][12][13] Several machine learning (ML) and deep learning algorithms have been developed to categorize fish species. For instance, Jager et al 14 employed AlexNet architecture for feature extraction and multiclass SVM for classification, whereas hierarchical features and support vector machine (SVM) are used for fish classification.…”

Section: Introductionmentioning

confidence: 99%

An enhanced YOLOv5 model for fish species recognition from underwater environments

Shah

Alaba

Nabi³

et al. 2023

Ocean Sensing and Monitoring XV

Self Cite

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Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It is a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce the YOLOv5 model for the recognition of fish species that can be implemented as an object detection model for analyzing multiple fishes in a single image. Moreover, we have modified the depth scale of different layers in the backbone of the YOLOv5 model to obtain improved results on fish species recognition. In addition, we have implemented a transformer block in the backbone network and introduced a class balance loss function to obtain enhanced performance. It can perform fish species recognition as an object detection approach by classifying each of the fish species in addition to localizing for the estimation of the position and size of the fish in an image. Experiments are conducted on the fine-grained and large-scale reef fish dataset that we have obtained from the Gulf of Mexico -the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an enhanced YOLOv5 model can yield better detection results in comparison to YOLOv5 for underwater fish species recognition.

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“…So, sensors have limitations, and no sensors fit all applications. 1 Multimodal fusion is commonly used for autonomous driving to reduce the effect of sensor limitations, provide complementary knowledge from different sensors, such as color from the camera and 3D information from LiDAR, and use redundant data in the case of sensor failure.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, there is also an error when a camera image is converted into pseudo-LiDAR representation. 1 Additionally, there is no best and most agreed way of fusing multimodal data yet. Recently, the transformer network has gotten attention for vision tasks, including multimodal fusion.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor fusion 3D object detection for autonomous driving

Alaba

Ball

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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Three-dimensional object detection is vital for understanding the autonomous vehicle driving environment. Different sensors are used for this purpose, such as cameras and LiDARs. Camera sensors are rich in color and texture information. However, cameras are unsuitable for 3D object detection due to the lack of depth information. Additionally, camera sensors are vulnerable to bad weather, such as snow, fog, and night driving. Autonomous driving needs a fast and accurate perception system for robust operation of the following pipeline, such as path planning and control. LiDAR is a commonly used sensor for 3D object detection because of its 3D information. However, its lack of color and texture information reduces the classification and detection performance. Therefore, there is no complete sensor that works for all situations. In this work, we propose a multi-modal fusion 3D object detection model for autonomous driving to use the best out of LiDAR and camera sensors. The model comprises a feature extraction network and a fusion network. Feature extraction networks transform the image and point cloud data into high-level features before fusion. Then, features from images and LiDAR data are fused. The experimental result on the nuScenes dataset shows the model's competitive performance for 3D object detection.

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Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review

Cited by 44 publications

References 121 publications

Active learning-based pedestrian and road sign detection

Active learning-based pedestrian and road sign detection

An enhanced YOLOv5 model for fish species recognition from underwater environments

Multi-sensor fusion 3D object detection for autonomous driving

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