Explainable Object Detection

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8 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection
Sep 17, 2026Sarvenaz Sardari, Freddy Fernandes, Samarth Yelvande +2Explainable Object Detection Explainable Artificial Intelligence
Mercedes-Benz AG, Future Automotive Manufacturing, Benz-Str. Bau40, 71063 Sindelfingen, Germany · University of Stuttgart, AI, Intelligent Sensing and Perception, Universitätsstr. 38, 70569 Stuttgart, Germany
Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Sep 15, 2026Jiayi Zhou, David W. Johnston, Brinnae BentExplainable Artificial Intelligence Explainable Object Detection
Pratt School of Engineering, Duke University, Durham, North Carolina, United States · Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University, Durham, North Carolina, United States
EIVE: End-to-End Instance-Specific Visual Explanations for Detection Transformers
Jun 1, 2026Jianlin Xiang, Yanshan Li, Linhui DaiTransformer Interpretability Feature Attribution
Institute of Intelligent Information Processing, Shenzhen University; the Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University; and the Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University, Shenzhen, China
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
May 20, 2026Wanying Tan, Shuo Yan, Dazhi Huang +7Explainable Artificial Intelligence Small Object Detection
Shenzhen University, Shenzhen, China · The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou,2026 China +2
PIEDet: Prototype-Driven Intrinsically Explainable Object Detection
Apr 15, 2026Jianlin Xiang, Linhui Dai, Xue Yang +2Explainable Object Detection Object Detection
Institute of Intelligent Information Processing, Shenzhen University · the Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University · the Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University +1
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
Mar 24, 2026Marios Impraimakis, Daniel Vazquez, Feiyu ZhouExplainable Object Detection Object Detection
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK. · Department of Civil Engineering, Zhejiang University, Hangzhou 310027, China.
Concept-based explanations of Segmentation and Detection models in Natural Disaster Management
Mar 24, 2026Samar Heydari, Jawher Said, Galip Ümit Yolcu +7Explainable Object Detection Efficient Semantic Segmentation
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany · Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Feb 24, 2026Xueqiang Lv, Shizhou Zhang, Yinghui Xing +3Explainable Object Detection Object Detection
School of Computer Science, Northwestern Polytechnical Unviersity, Xi’an, China · Huawei Technologies Ltd.

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Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection

Sarvenaz Sardari, Freddy Fernandes, Samarth Yelvande, Jose Moises Araya-Martinez+1

Mercedes-Benz AG, Future Automotive Manufacturing, Benz-Str. Bau40, 71063 Sindelfingen, Germany · University of Stuttgart, AI, Intelligent Sensing and Perception, Universitätsstr. 38, 70569 Stuttgart, Germany

Explainable Object Detection Explainable Artificial Intelligence VLM Adaptation Explainability Evaluation Industrial Inspection Object Detection

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Explainable Artificial Intelligence (XAI) solutions are essential for building trust in AI technologies and their integration in real manufacturing lines. However, most existing methods are tailored to technical experts, limiting their accessibility to diverse user groups such as blue-collar workers in manufacturing lines who use AI for quality control. In this work, we introduce an XAI interface for object detection in industrial manufacturing based on a fine-tuned vision-language model, designed to generate intuitive explanations for non-expert users. We benchmark existing vision-language models and demonstrate that out-of-the-box models often fall short in delivering clear, context-relevant explanations for non-expert users. To address this, we fine-tune a vision-language model and integrate it into our interface, enabling contextualized, accessible explanations for non-expert users. We demonstrate improvements in explanation clarity, instruction adherence, image groundedness, and contextual awareness over GPT 4o-mini on proprietary and public robotics dataset. This approach advances the accessibility and usability of AI explanations, making them more intuitive and applicable in manufacturing domain.