Object Detection in Self Driving Cars Using Deep Learning

Prajwal, P; Prajwal, D; Harish, D H; Gajanana, R; S, Jayasri B; Lokesh, S.

doi:10.1109/icses52305.2021.9633965

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…The projection features p 0 and {q k } , k ∈ [0, n − 1] are obtained by means of a linear projection function φ . Its effect is to slice and dice the input features according to the number of channels, as shown in Eq (4).…”

Section: Efficient Space-time Interaction Modulementioning

confidence: 99%

“…Anchor frame size Tiny detection head [3,4], [4,9], [8,6] Small detection head [7,13], [13,9], [12,17] Medium detection head [23,13], [19,23], [41,21] Large detection head [27,44], [59,40], [80,86] Table 1. Anchor frame size for the four inspection heads of the GBS-YOLOv5 model…”

Section: Detection Headmentioning

confidence: 99%

“…Since the 20th century, object detection has become an important research result in the field of computer vision. It has been widely developed and applied in the fields of intelligent transportation, pedestrian detection and driverless driving [1][2][3][4] . As the accuracy of image classification improves, object detection algorithms based on convolutional neural networks 5,6 have gradually become an current research hotspot.…”

Section: Introductionmentioning

confidence: 99%

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GBS-YOLOv5: Improved Algorithm for UAV Intelligent Transportation Based on YOLOv5

Liu

Duan

Lou

et al. 2023

Preprint

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As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. UAVs are less accurate at detecting drones because they operate from the air and take pictures of small targets. To address the problem of low accuracy of UAVs in detecting small targets, we proposed the GBS-YOLOv5 model. It was an improvement on the original YOLOv5 model. The default YOLOv5 model suffered from severe loss of small target information and under-utilization of shallow feature information as the depth of the feature extraction network deepened. We deepened the feature extraction network by introducing an efficient spatiotemporal interaction (ESI) module. We added the spatial pyramid convolution (SPC) module as a detection head for tiny-sized targets. A shallow bottleneck (SB) was introduced to better preserve the detailed information of small targets in the shallow features. The introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3% and the mAP@0.5:0.95 was 20.0%. Compared to the default YOLOv5 algorithm was boosted by 4.0% and 3.5% respectively.

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Section: Efficient Space-time Interaction Modulementioning

confidence: 99%

Section: Detection Headmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GBS-YOLOv5: Improved Algorithm for UAV Intelligent Transportation Based on YOLOv5

Liu

Duan

Lou

et al. 2023

Preprint

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“…This research paper was carried out by Ruturaj Kulkarni et al (2018) [10] introduces a robust deep neural network model that employs transfer learning for the accurate detection and recognition of traffic lights. To facilitate object detection in self-driving cars using deep learning, P Prajwal et al (2021) [11] have selected the SSD model in conjunction with the Mo-bileNet neural network as the foundational architecture due to its ability to produce results rapidly while maintaining a moderate level of accuracy. VD Nguyen et al (2018) [12] presents a comprehensive framework that combines deep learning techniques, multiple local patterns, and depth information to identify, classify, and monitor vehicles and walkers on the road.…”

Section: Introductionmentioning

confidence: 99%

Advanced Night time Object Detection in Driver-Assistance Systems using Thermal Vision and YOLOv5

Vo¹,

Quach²

2023

IJACSA

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Driver-assistance systems have become an indispensable component of modern vehicles, serving as a crucial element in enhancing safety for both drivers and passengers. Among the fundamental aspects of these systems, object detection stands out, posing significant challenges in low-light scenarios, particularly during nighttime. In this research paper, we propose an innovative and advanced approach for detecting objects during nighttime in driver-assistance systems. Our proposed method leverages thermal vision and incorporates You Only Look Once version 5 (YOLOv5), which demonstrates promising results. The primary objective of this study is to comprehensively evaluate the performance of our model, which utilizes a combination of stochastic gradient descent (SGD) and Adam optimizer. Moreover, we explore the impact of different activation functions, including SiLU, ReLU, Tanh, LeakyReLU, and Hardswish, on the efficiency of nighttime object detection within a driver assistance system that utilizes thermal imaging. To assess the effectiveness of our model, we employ standard evaluation metrics including precision, recall, and mean average precision (mAP), commonly used in object detection systems.

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