Deep Learning Based, Real-Time Object Detection for Autonomous Driving

Akyol, Gamze; Kantarcı, Alperen; Çelik, A.; Ak, Abdullah Cihan

doi:10.1109/siu49456.2020.9302500

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…The convolutional neural network (CNN) possesses many advantages over YOLO in feature learning, local feature capture, data expansion, pre-training, and adaptability to classification tasks [36]. In facial emotion detection, YOLO (You Only Look Once) is a target detection framework belonging to convolutional neural networks (CNN), whereas facial expression detection focuses on recognizing and classifying emotions conveyed through facial expressions.…”

Section: ) Emotion Detection Using Cnn Modulementioning

confidence: 99%

Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism

Ma,

Fu,

Rakheja

et al. 2024

IEEE Access

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An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver's emotion. Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a CNN module are synthesized to ensure improved performance in terms of accuracy and convergence, where MHSA is used to detect distracted driving behavior and CNN is used to detect driver's emotion. The FER2013 dataset and collected dataset are used to train the improved YOLOv8. The training results show that the proposed YOLOv8 demonstrates improved performance compared with the commonly used YOLO based methods. Finally, the validity of the proposed YOLOv8 method is illustrated through implementations in Jetson Nano platform, where the TensorRT and DeepStream methods in the Jetson Nano device are used to optimize the volume and operational speed of the proposed YOLOv8 method, respectively. Test results show that the proposed YOLOv8 method can yield better real-time and accuracy properties.

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Section: ) Emotion Detection Using Cnn Modulementioning

confidence: 99%

Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism

Ma,

Fu,

Rakheja

et al. 2024

IEEE Access

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“…Recent studies using the variants of the You Only Look Once (Yolo) algorithm, which is based on a convolutional neural network (CNN), have vastly improved the performance of real-time object detection [6][7][8]. Another approach for object detection using CNN is semantic segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Valada et al [5] demonstrated that ParseNet and AdapNet show high accuracy in detecting objects in images with severe driving conditions. Many studies with segmentation have improved object detection performance, but the accuracy still stays around 80% [5][6][7][8][9][10]. The accuracy of most segmentation algorithms is higher than Yolo algorithms', but the efficiency is much worse [11][12][13].…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Structure for Semantic Segmentation of Roadway Images with Limited Visibility Using Deep Artificial Neural Networks

An,

Mohan,

Leppo

et al. 2023

Preprint

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The field of autonomous driving leaves minimal margins for error. Ensuring that self-driving vehicles possess the ability to accurately perceive their surroundings, even amidst conditions of limited visibility, is of utmost importance. We propose a novel approach to enhance the precision of object detection on the road during limited visibility driving or roadway conditions. The initial step involves the classification of the driving condition of an input image, and then, the corresponding semantic segmentation model will process the image to distinguish objects. Our dataset consists of roadway images depicting 20 distinct objects amidst adverse limited visibility conditions. The experimental results validate our approach, with the proposed method displaying quality accuracy levels for training, validation, and testing data. Our classification model achieved 100% accuracy. Particularly, the proposed methods achieved final mean IoU scores of 57.3%, 32.0%, 49.4%, and 47.8%, respectively, for FOG, NIGHT, RAIN, and SNOW conditions when using the U-NET model for segmentation. These mean IoU results are better than the traditional nonhierarchical training methods, which utilize the same U-NET structure.

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“…Target detection has become one of the most essential topics in the CV community, requiring object classification and localization [ 3 ]. It has a variety of applications including autonomous driving [ 4 ], medical lesion detection [ 5 ], intelligent security [ 6 ], disaster management [ 7 ], agriculture surveys [ 8 ], urban planning [ 9 ], geographic information system updating [ 10 ], and many more. Small target detection task scenarios have important application value in various fields.…”

Section: Introductionmentioning

confidence: 99%

ST-CenterNet: Small Target Detection Algorithm with Adaptive Data Enhancement

Guo

2023

Entropy

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General target detection with deep learning has made tremendous strides in the past few years. However, small target detection sometimes is associated with insufficient sample size and difficulty in extracting complete feature information. For safety during autonomous driving, remote signs and pedestrians need to be detected from driving scenes photographed by car cameras. In the early period of a medical lesion, because of the small area of the lesion, target detection is of great significance to detect masses and tumors for accurate diagnosis and treatment. To deal with these problems, we propose a novel deep learning model, named CenterNet for small targets (ST-CenterNet). First of all, due to the lack of visual information on small targets in the dataset, we extracted less discriminative features. To overcome this shortcoming, the proposed selective small target replication algorithm (SSTRA) was used to realize increasing numbers of small targets by selectively oversampling them. In addition, the difficulty of extracting shallow semantic information for small targets results in incomplete target feature information. Consequently, we developed a target adaptation feature extraction module (TAFEM), which was used to conduct bottom-up and top-down bidirectional feature extraction by combining ResNet with the adaptive feature pyramid network (AFPN). The improved new network model, AFPN, was added to solve the problem of the original feature extraction module, which can only extract the last layer of the feature information. The experimental results demonstrate that the proposed method can accurately detect the small-scale image of distributed targets and simultaneously, at the pixel level, classify whether a subject is wearing a safety helmet. Compared with the detection effect of the original algorithm on the safety helmet wearing dataset (SHWD), we achieved mean average precision (mAP) of 89.06% and frames per second (FPS) of 28.96, an improvement of 18.08% mAP over the previous method.

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Deep Learning Based, Real-Time Object Detection for Autonomous Driving

Cited by 12 publications

References 9 publications

Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism

Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism

Hierarchical Structure for Semantic Segmentation of Roadway Images with Limited Visibility Using Deep Artificial Neural Networks

ST-CenterNet: Small Target Detection Algorithm with Adaptive Data Enhancement

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