Dealing with Robustness of Convolutional Neural Networks for Image Classification

Arcaini, Paolo; Bombarda, Andrea; Bonfanti, Silvia; Gargantini, Angelo

doi:10.1109/aitest49225.2020.00009

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…By automatically learning features from the images, DL models can accurately identify and classify different disease symptoms, reducing the need for manual feature engineering ( Drenkow et al., 2021 ). Additionally, these models can handle large amounts of data, making them well-suited for large-scale plant lesions detection ( Arcaini et al., 2020 ). Therefore, in review paper, we evaluate the current state-of-the-art in using DL for plant lesions recognition, examining various architectures, techniques, and datasets used in this field.…”

Section: Deep Learning Approaches For Recognizing Imagesmentioning

confidence: 99%

An advanced deep learning models-based plant disease detection: A review of recent research

et al. 2023

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Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

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Section: Deep Learning Approaches For Recognizing Imagesmentioning

confidence: 99%

An advanced deep learning models-based plant disease detection: A review of recent research

et al. 2023

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“…This is not completely in line with expectations from related work. Convolutional neural networks are known to have robustness issues ( Uličnỳ et al, 2016 ; Ghosh et al, 2018 ; Arcaini et al, 2020 ). For example, if electrodes are placed differently on day 2 and MVC values differ from day to day, the EMG envelope images that are created can differ too much from day 1 for the CNN to make a good prediction.…”

Section: Discussionmentioning

confidence: 99%

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

et al. 2022

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Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

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“…In the context of autonomous driving Tian et al's work on DeepTest [3] has gained great attention. Other similar work include [4] and most recently [5]. However, there has also been work on successfully preventing vision systems from being attacked using adversarial examples (see [6]).…”

Section: Lack Of Knowledge In Terms Of How Which Failures Influence T...mentioning

confidence: 99%

A framework for the automation of testing computer vision systems

Wotawa

Klampfl

Jahaj

2021

2021 IEEE/ACM International Conference on Automation of Software Test (AST)

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Dealing with Robustness of Convolutional Neural Networks for Image Classification

Cited by 12 publications

References 25 publications

An advanced deep learning models-based plant disease detection: A review of recent research

An advanced deep learning models-based plant disease detection: A review of recent research

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

A framework for the automation of testing computer vision systems

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