Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models. (January 2023)

• Record Type: Journal Article
• Authors: Magalhães, Sandro Costa; dos Santos, Filipe Neves; Machado, Pedro; Moreira, António Paulo; Dias, Jorge
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: 62M45 -- 62P30 -- 68Q85; Embedded systems -- Heterogeneous platforms -- Object detection -- SSD resNet -- RetinaNet resNet; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105604
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Purpose: Visual perception enables robots to perceive the environment. Visual data is processed using computer vision algorithms that are usually time-expensive and require powerful devices to process the visual data in real-time, which is unfeasible for open-field robots with limited energy. This work benchmarks the performance of different heterogeneous platforms for object detection in real-time. This research benchmarks three architectures: embedded GPU—Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB, and NVIDIA Jetson TX2), TPU—Tensor Processing Unit (such as Coral Dev Board TPU), and DPU—Deep Learning Processor Unit (such as in AMD/Xilinx ZCU104 Development Board, and AMD/Xilinx Kria KV260 Starter Kit). Methods: The authors used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset. After the trained model was converted and compiled for target-specific hardware formats to improve the execution efficiency. Conclusions and Results: The platforms were assessed in terms of performance of the evaluation metrics and efficiency (time of inference). Graphical Processing Units (GPUs) were the slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays (FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson TX2. TPU and GPU are the most power-efficient, consuming about 5 W. The performance differences, in the evaluation metrics, across devices are irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of about 60 %. Graphical abstract: Highlights: RetinaNet ResNet50 and SSD ResNet50 can be successfully executed in embedded GPUs, TPUs, and FPGAs. FPGAs are the fastest devices for executing deep learning models and ANNs. TPUs are the most power-efficient devices for embedded applications. GPUs offer a good balance between implementation and deployment of ANNs.