Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Ponn, Thomas; Kröger, Thomas; Diermeyer, Frank

doi:10.3390/s20133699

Cited by 31 publications

(13 citation statements)

References 34 publications

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“…The second is to extract features that can be explained through a trained deep learning model. The third one is to analyze the cause by evaluating the learning outcome regardless of the model, such as the local interpretable model-agnostic explanations (LIME) or the SHAP algorithm [ 35 , 36 , 37 ]. Unlike in the past, developments in computing power have made it easier to apply xAI, as the problem of interpretation has been solved.…”

Section: Proposed Methodologymentioning

confidence: 99%

Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Hong

Lee

et al. 2020

Sensors

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This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. The proposed deep learning model affords a significantly improved accuracy by organizing networks with a one-dimensional convolutional neural network, long short-term memory, and bidirectional long short-term memory. In particular, this paper investigates two practical and crucial issues in applying the deep learning model for system prognosis. The first is the requirement of numerous sensors for different components, i.e., the curse of dimensionality. Second, the deep neural network cannot identify the problematic component of the turbofan engine due to its “black box” property. This study thus employs dimensionality reduction and Shapley additive explanation (SHAP) techniques. Dimensionality reduction in the model reduces the complexity and prevents overfitting, while maintaining high accuracy. SHAP analyzes and visualizes the black box to identify the sensors. The experimental results demonstrate the high accuracy and efficiency of the proposed model with dimensionality reduction and show that SHAP enhances the explainability in a conventional deep learning model for system prognosis.

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Section: Proposed Methodologymentioning

confidence: 99%

Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Hong

Lee

et al. 2020

Sensors

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“…IOU (intersection over union) [ 36 ] is used to evaluate the positioning accuracy effect. In object detection tasks using images, common evaluation indices include average precision (AP) [ 14 ] and mean average precision (mAP) [ 38 ]. AP represents the average precision, which is the average value of the highest precision rate under different recall rates.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

Qiang

Chen

Zhou

et al. 2020

Sensors

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In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

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“…However, Sequential Rule Mining (SRM) [274] algorithms were found to be exploited in all task categories of design space except prediction. Among other methods available for adding explainability to intelligent systems, Local Interpretable Model-Agnostic Explanations (LIME) [270,272,275,276] and Shapley Additive Explanations (SHAP) [21,271,272,277] are worth mentioning due to their wide acceptability among researchers.…”

Section: Xai In Terms Of Design Spacementioning

confidence: 99%

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

et al. 2022

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Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.

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Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Cited by 31 publications

References 34 publications

Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

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