Multi-Pole Road Sign Detection Based on Faster Region-based Convolutional Neural Network (Faster R-CNN)

Syaharuddin, Achmad Zulfajri; Zainuddin, Zainal Abidin; Andani,

doi:10.1109/aims52415.2021.9466014

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…www.ijacsa.thesai.org Mean average precision is a well-known and commonly used object detection evaluation metric. Faster R-CNN [35], YOLO [36], and MobileNet [37] are all state-of-the-art models that use mAP to evaluate their models. We tried to test the performance of the three YOLOv5 modelssmall, medium, and largein our implementation.…”

Section: Results Analysismentioning

confidence: 99%

Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

Rafi¹,

Chakma²,

Mahmud³

et al. 2022

IJACSA

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For years, humans have pondered the possibility of combining human and machine intelligence. The purpose of this research is to recognize vehicles from media and while there are multiple models associated with this, models that can detect vehicles commonly used in developing countries like Bangladesh, India, etc. are scarce. Our focus was to assimilate the largest dataset of vehicles exclusive to South Asia in addition to the more common universal vehicles and apply it to track and recognize these vehicles, even in motion. To develop this, we increased the class variations and quantity of the data and used multiple variations of the YOLOv5 model. We trained different versions of the model with our dataset to properly measure the degree of accuracy between the models in detecting the more unique vehicles. If vehicle detection and tracking are adopted and implemented in live traffic camera feeds, the information can be used to create smart traffic systems that can regulate congestion and routing by identifying and separating fast and slow-moving vehicles on the road. The comparison between the three different YOLOv5 models led to an analysis that indicates that the large variant of the YOLOv5 architecture outperforms the rest.

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Section: Results Analysismentioning

confidence: 99%

Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

Rafi¹,

Chakma²,

Mahmud³

et al. 2022

IJACSA

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“…One year later, Girshick [14] improved the RCNN into Fast-RCNN and Faster-RCNN. Right now, Faster-RCNN has become a popular two-stage algorithm for object detection [15,16]. On the other hand, single-stage object detection, such as single shot multibox detector (SSD) [17] and you only look once (YOLO) series, has greatly improved the image processing speed.…”

Section: Related Work 21 Object Detectionmentioning

confidence: 99%

A Novel Approach of Hybrid Bounding Box Regression Mechanism to Improve Convergency Rate and Accuracy

2024

IJIES

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Bounding box regression is a commonly used technique aimed at enhancing the precision of object localization, which is crucial in the field of object recognition. The intersection over union (IoU) metric, which calculates the overlap between predicted and ground truth bounding boxes, is frequently used to evaluate the performance of object detection models. However, the MSE loss function used previously is not compatible with the IoU-based evaluation and has shown sensitivity to differences in object scales. The use of IoU as the basis for loss functions has become more common in recent years, and as a result, new techniques such as the Generalized IoU (GIoU) and complete IoU (CIoU) losses have grown to be developed. This paper introduces a hybrid mechanism called GCIoU loss, which combines GIoU and CIoU losses with the aim of further enhancing localization accuracy and convergence speed. According to our findings, the average precision (AP) is greatly improved by the GCIoU loss in comparison to the GIoU and CIoU losses by 7.72% (highest) to the basis of IoU loss, and 0.87% improvement to the CIoU loss. The GCIoU loss performs consistently well across different thresholds, especially at higher levels, and has improved AP75 by 3.07% compared to the CIoU loss as the default configuration. Additionally, GCIoU loss converges faster and even more robustly in the simulation experiments by taking 14% fewer epochs than the CIoU loss, leading to localization more precisely. By this, GCIoU loss is showing its usefulness in object identification and model optimization.

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“…The most typical algorithms for object detection with deep learning techniques are the two-stage detection algorithms based on anchor boxes, and the single-stage detection algorithms based on anchor-free boxes. The former includes R-CNN [26][27], Fast R-CNN [28], Faster R-CNN [29][30], R-FCN [31][32] and Mask R-CNN [33]. This kind of algorithms usually has a high accuracy but spends much time in detection.…”

Section: B Object Detectionmentioning

confidence: 99%

STBi-YOLO: A Real-Time Object Detection Method for Lung Nodule Recognition

Liu

2022

IEEE Access

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Lung cancer is the most prevalent and deadly oncological disease in the world, but a timely detection of lung nodules can greatly improve the survival rate of this disease. However, due to the tiny size of lung nodules and inconspicuous edges, lung nodules are not easily distinguished by naked eyes thus medical image diagnosticians are prone to misdiagnosis simply based on their own experiences and subjective judgements. In recent years, the machine-learning-based image processing techniques find their wide applications in the field of medical diagnosis, and have been proved to be an efficient way to aid diagnosticians to accurately identify subtle lesions in images. To accurately recognize lung nodules in CT images, in this paper, we propose an approach, called STBi-YOLO. This approach stems from YOLO-v5, but makes significant improvements from three dimensions-we first use the spatial pyramid pooling network that is based on stochastic-pooling method to modify the basic network structure of YOLO-v5; then apply a bidirectional feature pyramid network to perform multi-scale feature fusion; finally improve the loss function of the YOLO-v5 and adopt the EIoU function to optimize the training model. To evaluate our approach, we compare STBi-YOLO with YOLO-v3, YOLO-v4, YOLO-v5, and multiple leading object detection models, such as Faster R-CNN and SSD. The experiments show that STBi-YOLO achieves an accuracy of 96.1% and a recall rate of 93.3% for the detection of lung nodules, while producing a 4× smaller model size in memory consumption than YOLO-v5 and exhibiting comparable results in terms of mAP and time cost against Faster R-CNN and SSD.

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Multi-Pole Road Sign Detection Based on Faster Region-based Convolutional Neural Network (Faster R-CNN)

Cited by 8 publications

References 6 publications

Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

A Novel Approach of Hybrid Bounding Box Regression Mechanism to Improve Convergency Rate and Accuracy

STBi-YOLO: A Real-Time Object Detection Method for Lung Nodule Recognition

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