An hardware-aware image polarity detector enhanced with visual attention

Ragusa, Edoardo; Apicella, Tommaso; Gianoglio, Christian; Zunino, Rodolfo; Gastaldo, Paolo

doi:10.1109/ijcnn48605.2020.9206826

Cited by 3 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extends previous research [15], and presents a design strategy to deploy image polarity detection tools on resource-constrained embedded systems; it introduces implementation details, an extensive analysis about hardware options, and new experiments. Mobile devices are the target platforms, hosting on-line polarity detectors supported by hardware-oriented deep neural networks.…”

Section: Introductionmentioning

confidence: 79%

Design and Deployment of an Image Polarity Detector with Visual Attention

et al. 2021

Self Cite

View full text Add to dashboard Cite

Embedding the ability of sentiment analysis in smart devices is especially challenging because sentiment analysis relies on deep neural networks, in particular, convolutional neural networks. The paper presents a novel hardware-friendly detector of image polarity, enhanced with the ability of saliency detection. The approach stems from a hardware-oriented design process, which trades off prediction accuracy and computational resources. The eventual solution combines lightweight deep-learning architectures and post-training quantization. Experimental results on standard benchmarks confirmed that the design strategy can infer automatically the salient parts and the polarity of an image with high accuracy. Saliency-based solutions in the literature prove impractical due to their considerable computational costs; the paper shows that the novel design strategy can deploy and perform successfully on a variety of commercial smartphones, yielding real-time performances.

show abstract

Section: Introductionmentioning

confidence: 79%

Design and Deployment of an Image Polarity Detector with Visual Attention

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…This section illustrates the results of the experiments performed in the three test scenarios. According to our previous findings [ 38 ] we observed that moving the same architecture from the Desktop to the embedded platform has negligible impact on the detection accuracy, while it mostly affects speed and memory footprint. Therefore, in Section 5.1 , Section 5.2 and Section 5.3 we first illustrate, for each scenario, our achievements using the Desktop platform and, then, in Section 5.4 , we analyze the impact of deploying T-RexNet on the Jetson Nano.…”

Section: Resultsmentioning

confidence: 56%

“…Accordingly, a direct measure would yield biased results. Indeed, literature proves that similar models can be deployed in devices using a smaller memory footprint [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

“…TensorTRT can adopt different data sizes when deploying a network: standard floating-point representation (FP32), half-precision floating point (FP16) and 8-bit integer representation (INT8). The experiments were conducted with the FP16 format since this provides a good trade-off between accuracy and power consumption [ 38 ]. In addition, the results proved that FP16 is indeed sufficient to reach good frame rates using Jetson Nano.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects

Canepa

Ragusa

Zunino

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.

show abstract

Ten Years of Sentic Computing

Susanto

Wang

et al. 2021

Cogn Comput

View full text Add to dashboard Cite

An hardware-aware image polarity detector enhanced with visual attention

Cited by 3 publications

References 24 publications

Design and Deployment of an Image Polarity Detector with Visual Attention

Design and Deployment of an Image Polarity Detector with Visual Attention

T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects

Ten Years of Sentic Computing

Contact Info

Product

Resources

About