Fast and accurate object detection in high resolution 4K and 8K video using GPUs

Růžička, Vít; Franchetti, Franz

doi:10.1109/hpec.2018.8547574

Cited by 43 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the work in [12] addressed the issue that the majority of deep learning object detection models work with low resolution images since they are the publicly available datasets and are time efficient to train. Their goal is to detect small objects in 4k images without losing information due to downscaling.…”

Section: Related Workmentioning

confidence: 99%

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

et al. 2021

View full text Add to dashboard Cite

Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.

show abstract

Section: Related Workmentioning

confidence: 99%

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

et al. 2021

View full text Add to dashboard Cite

show abstract

“…YOLO is constantly used in general object detection with customization. Paper [31] provides techniques for fast and accurate object detection. High-resolution data is downscaled and divided into parts for detection.…”

Section: Yolo Modelsmentioning

confidence: 99%

Object Detection in Last Decade - A Survey

Arshad

2021

SJI

View full text Add to dashboard Cite

In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is a challenging and most fundamental problem. Researchersin the last decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of the last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in the last decade and the future is promising much more advancement inobject detection on the basis of work done in this decade.In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is the challenging and most fundamental problem. Researchersinlast decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in last decade and future is promising much more advancement inobject detection on the basis of work done in this decade.

show abstract

“…Jadhav et al [77] modified the anchor scale and Tang et al [84] designed a coarse anchor-free detector (CPEN) to address dense small object detection. In [85]- [87], the authors proposed effective solutions to small object detection from high-resolution images by cropping multiple subset tiles from the original highresolution images, and learning them through use of a CNN network without degrading the resolution.…”

Section: Object Detection On Small Objectsmentioning

confidence: 99%

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

View full text Add to dashboard Cite

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE Xplore. Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

show abstract

Fast and accurate object detection in high resolution 4K and 8K video using GPUs

Cited by 43 publications

References 5 publications

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

Object Detection in Last Decade - A Survey

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Contact Info

Product

Resources

About