Adaptive Real-Time Object Detection for Autonomous Driving Systems

Hemmati, Maryam; Biglari-Abhari, Morteza; Niar, Smaïl

doi:10.3390/jimaging8040106

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Florea et al [ 16 ] presented a real-time perception component based on the low-level fusion between point clouds and semantic scene information. Furthermore, Hemmati et al [ 22 ] presented an adaptive system with a hardware-software co-design on Zynq UltraScale+ MPSoC that detects pedestrians and vehicles in different lighting conditions on the road.…”

Section: Related Workmentioning

confidence: 99%

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Pyo

Bae

Joo

et al. 2022

Sensors

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Autonomous driving and its real-world implementation have been among the most actively studied topics in the past few years. In recent years, this growth has been accelerated by the development of advanced deep learning-based data processing technologies. Moreover, large automakers manufacture vehicles that can achieve partially or fully autonomous driving for driving on real roads. However, self-driving cars are limited to some areas with multi-lane roads, such as highways, and self-driving cars that drive in urban areas or residential complexes are still in the development stage. Among autonomous vehicles for various purposes, this paper focused on the development of autonomous vehicles for garbage collection in residential areas. Since we set the target environment of the vehicle as a residential complex, there is a difference from the target environment of a general autonomous vehicle. Therefore, in this paper, we defined ODD, including vehicle length, speed, and driving conditions for the development vehicle to drive in a residential area. In addition, to recognize the vehicle’s surroundings and respond to various situations, it is equipped with various sensors and additional devices that can notify the outside of the vehicle’s state or operate it in an emergency. In addition, an autonomous driving system capable of object recognition, lane recognition, route planning, vehicle manipulation, and abnormal situation detection was configured to suit the vehicle hardware and driving environment configured in this way. Finally, by performing autonomous driving in the actual experimental section with the developed vehicle, it was confirmed that the function of autonomous driving in the residential area works appropriately. Moreover, we confirmed that this vehicle would support garbage collection works through the experiment of work efficiency.

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Section: Related Workmentioning

confidence: 99%

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Pyo

Bae

Joo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Object detection is a fundamental problem in computer vision that simultaneously classifies and localizes all objects in images or videos [ 1 , 2 , 3 ]. With the fast development of deep learning, object detection has achieved great success and been applied to many real-world tasks such as object tracking [ 4 , 5 ], image classification [ 6 , 7 , 8 ], segmentation [ 9 , 10 ], self-driving [ 11 ], and medical image analysis [ 12 , 13 ]. Generally speaking, the detection models could be categorized as two-stage detectors and one-stage detectors.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs

et al. 2022

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Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize the fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple while effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives.

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Development of Autonomous Driving for Passenger Boarding Bridge (PBB) Through Aircraft Door Detection Based on YOLO Object Detection Algorithm

Weon,

Park,

Kim

et al. 2024

Lecture Notes in Networks and Systems

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Adaptive Real-Time Object Detection for Autonomous Driving Systems

Cited by 7 publications

References 24 publications

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas

Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs

Development of Autonomous Driving for Passenger Boarding Bridge (PBB) Through Aircraft Door Detection Based on YOLO Object Detection Algorithm

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