An Improved Faster R-CNN Method for Car Front Detection

Yu, Guo; Yu, Pengfei; Li, Haiyan; Li, Hongsong

doi:10.1109/iaeac54830.2022.9929605

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, some scholars combined the advantages of single-stage and two-stage algorithms to propose an improved single-stage algorithm Refine Net, without anchors. box algorithm CormorNet [18] with CenterNet [19], algorithm for improving network structure and residual structure [20,21], algorithm for improving loss function [22] have improved in accuracy or speed. Starting from the vehicle model, divides the re-identification problem into the recognition problem of different vehicles of the same model and the recognition problem of different models, and proposes a method based on multi-granularity learning to extract global appearance features of vehicles to solve the problem of high similarity between classes.…”

Section: Related Studies On Detection and Recognition Of Traffic Lightsmentioning

confidence: 99%

Grand challenge of image processing in automatic detection of vehicles running in red lights

Samonte,

Wang,

Tian

et al. 2024

Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024)

View full text Add to dashboard Cite

Section: Related Studies On Detection and Recognition Of Traffic Lightsmentioning

confidence: 99%

Grand challenge of image processing in automatic detection of vehicles running in red lights

Samonte,

Wang,

Tian

et al. 2024

Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024)

View full text Add to dashboard Cite

“…The first process is RPN takes an image as input and generates a set of region proposals, which are candidate bounding boxes that may contain objects. The Fast R-CNN detector takes the region proposals and the feature map from the RPN as input and performs classification and regression on each proposal to produce the final object detection results [20].…”

Section: Faster R-cnnmentioning

confidence: 99%

Forensic Analysis of Drones Attacker Detection Using Deep Learning

Editya,

Kurniati,

Alamin

et al. 2024

SJI

View full text Add to dashboard Cite

Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.

show abstract