A survey on parallel computing for traditional computer vision

Jaiswal, Deepak; Kumar, Praveen

doi:10.1002/cpe.6638

Cited by 4 publications

(2 citation statements)

References 85 publications

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“…The development in computer vision technology can be classified into conventional image processing techniques and deep-learning technology. Prior to the invention of graphic processing units, the classification, feature extraction, and region segmentation of images relied on conventional image-processing techniques, which employed inferences based on mathematical theories to accomplish the objective [9]. In convolution-based image processing, the image is fuzzified, and edge detection is conducted to extract features from the image; the extracted features are subsequently employed to facilitate the identification of information in images by the computer.…”

Section: Fundamentals Of Yolo Deep-learning Networkmentioning

confidence: 99%

Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

Ma,

Shen,

Huang

2023

Sensors

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A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding the emergent landing platform and for future reconnaissance area missions. This paper proposes an approach to enhance the visual capabilities of this system by using the You Only Look Once (YOLO)-based object detection (OD) with Tensor RTTM acceleration technique, an automated visual tracking gimbal camera control system, and multithread programing for image transmission to the ground station. With lightweight edge computing (EC), the mean average precision (mAP) was satisfied and we achieved a higher frame per second (FPS) rate via YOLO accelerated with TensorRT for an onboard UAV. The OD compares four YOLO models to recognize objects of interest for landing spots at the home university first. Then, the trained dataset with YOLOv4-tiny was successfully applied to another field with a distance of more than 100 km. The system’s capability to accurately recognize a different landing point in new and unknown environments is demonstrated successfully. The proposed approach substantially reduces the data transmission and processing time to ground stations with automated visual tracking gimbal control, and results in rapid OD and the feasibility of using NVIDIA JetsonTM Xavier NX by deploying YOLOs with more than 35 FPS for the UAV. The enhanced visual landing and future reconnaissance mission capabilities of real-time UAVs were demonstrated.

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Section: Fundamentals Of Yolo Deep-learning Networkmentioning

confidence: 99%

Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

Ma,

Shen,

Huang

2023

Sensors

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“…While HS and LK represent the current state-of-the-art in optical flow techniques and have been used as benchmarks to evaluate ad-hoc implementations in several platforms based on GPUs, FPGAs or DSPs [40][41][42], they still are pertinent in the embedded system scope. However, it is worth noting that numerous research endeavours have since addressed issues such as high-speed object detection, occlusion handling, illumination changes, and noise reduction.…”

Section: Image Processing For Optic Flowmentioning

confidence: 99%

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Faqir-Rhazoui,

García

2023

Preprint

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Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time, secure data processing near data sources, like smart devices, alleviating cloud computing energy use and saving network band-width. Specialized accelerators, like GPUs and FPGAs, are vital for low-latency edge computing but the requirements to customized code for different hardware and vendors supposes important compatibility issues. This paper evaluates the potential of SYCL in addressing code portability issues encountered in edge computing. We employed the Polybench suite to compare various SYCL implementations, specifically DPC++ and AdaptiveCpp, with the native solution, CUDA. The disparity between SYCL implementations was negligible, at just 5%. Furthermore, we evaluated SYCL in the context of specific edge computing applications such as video processing using three different optical flow algorithms. The results exposed a potential performance gap of 19% between CUDA and SYCL. This performance differential is the price one may need to pay when achieving the ability to successfully run the same code on two distinct edge boards with four different architectures, including x86/64 CPU, ARM CPU, NVIDIA GPU, and Intel GPU. These findings underscore SYCL’s capacity to increase productivity in term of development costs and facilitate the IoT deployment without being locked into a particular platform or manufacturer.

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SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Faqir-Rhazoui,

García

2024

J Supercomput

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Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time and secure data processing near data sources, like smart devices, alleviating cloud computing energy use, and saving network bandwidth. Specialized accelerators, like GPUs and FPGAs, are vital for low-latency edge computing but the requirements to customized code for different hardware and vendors suppose important compatibility issues. This paper evaluates the potential of SYCL in addressing code portability issues encountered in edge computing. We employed the Polybench suite to compare various SYCL implementations, specifically DPC++ and AdaptiveCpp, with the native solution, CUDA. The disparity between SYCL implementations was negligible, at just 5%. Furthermore, we evaluated SYCL in the context of specific edge computing applications such as video processing using three different optical flow algorithms. The results revealed a slight performance gap of 3% when transitioning from CUDA to SYCL. Upon evaluating energy consumption, the observed difference ranged from $$\pm 10\%$$ ± 10 % , depending on the application utilized. These gaps are the price one may need to pay when achieving the ability to successfully run the same code on two distinct edge boards. These findings underscore SYCL’s capacity to increase productivity in terms of development costs and facilitate IoT deployment without being locked into a particular platform or manufacturer.

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A survey on parallel computing for traditional computer vision

Cited by 4 publications

References 85 publications

Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

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