Deep Learning Based Vehicle Detection with Images Taken from Unmanned Air Vehicle

Böyük, Mustafa; Duvar, Ramazan; Urhan, Oğuzhan

doi:10.1109/asyu50717.2020.9259868

Cited by 17 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The properties of IOU (Intersection over Union) dictate that it cannot only determine the positive and negative, but can also be used to calculate the overlap between the predicted and true frames. IOU and IoULoss are defined in equations (5,6).…”

Section: Loss Functionsmentioning

confidence: 99%

Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach

Guo

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

View full text Add to dashboard Cite

Low resolution object detection could be challenging. In this paper, we proposed a GAN-based real-time data augmentation algorithm for the transfer learning task of UAV vehicle detection from ImageNet, with improvements including using FocalLoss to replace the cross-entropy loss commonly used in the industry, as well as redesigning the target detection Head combination to improve the model's detection accuracy by 4% over original YOLOv5 model. We make it feasible for deployments on UAV-carried ARM systems.

show abstract

Section: Loss Functionsmentioning

confidence: 99%

Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach

Guo

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

View full text Add to dashboard Cite

show abstract

“…In this case, the label information of the VisDrone dataset and the number of categories are shown in Figure 7. Loss = aLoss ob j + bLoss box + cLoss cls (7) In this case, the Loss in training contains three main aspects of loss: rectangular box loss (Loss box ), confidence loss (Loss ob j ), and classification loss (Loss). Loss weights a, b, c are set to: 1.0, 0.05, 0.5 respectively.…”

Section: Datasetmentioning

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%

See 1 more Smart Citation

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

Liu

Duan

Lou

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…SSD might not provide advanced characteristics, which is bad for little items. More training data are needed [9]. The SVM algorithm does not do well with large data sets.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Using Deep Learning Algorithms

Mumtaz,

Ashraf,

Jhanjhi

2023

Preprint

View full text Add to dashboard Cite

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

show abstract

Deep Learning Based Vehicle Detection with Images Taken from Unmanned Air Vehicle

Cited by 17 publications

References 12 publications

Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach

Vehicle detection in UAV traffic videos using GAN online augmentation: a transfer learning approach

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

Moving Object Detection Using Deep Learning Algorithms

Contact Info

Product

Resources

About