Explaining What Machines See: XAI Strategies in Deep Object Detection Models

• URL: http://arxiv.org/abs/2509.01991v1
• Date: Tue, 02 Sep 2025 06:16:30 GMT
• Title: Explaining What Machines See: XAI Strategies in Deep Object Detection Models
• Authors: FatemehSadat Seyedmomeni, Mohammad Ali Keyvanrad,
• Abstract summary: Explainable Artificial Intelligence (XAI) aims to make model decisions more transparent, interpretable, and trust-worthy for humans.<n>This review provides a comprehensive analysis of state-of-the-art explainability methods specifically applied to object detection models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, deep learning has achieved unprecedented success in various computer vision tasks, particularly in object detection. However, the black-box nature and high complexity of deep neural networks pose significant challenges for interpretability, especially in critical domains such as autonomous driving, medical imaging, and security systems. Explainable Artificial Intelligence (XAI) aims to address this challenge by providing tools and methods to make model decisions more transparent, interpretable, and trust-worthy for humans. This review provides a comprehensive analysis of state-of-the-art explain-ability methods specifically applied to object detection models. The paper be-gins by categorizing existing XAI techniques based on their underlying mechanisms-perturbation-based, gradient-based, backpropagation-based, and graph-based methods. Notable methods such as D-RISE, BODEM, D-CLOSE, and FSOD are discussed in detail. Furthermore, the paper investigates their applicability to various object detection architectures, including YOLO, SSD, Faster R-CNN, and EfficientDet. Statistical analysis of publication trends from 2022 to mid-2025 shows an accelerating interest in explainable object detection, indicating its increasing importance. The study also explores common datasets and evaluation metrics, and highlights the major challenges associated with model interpretability. By providing a structured taxonomy and a critical assessment of existing methods, this review aims to guide researchers and practitioners in selecting suitable explainability techniques for object detection applications and to foster the development of more interpretable AI systems.

Related papers

• Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directions [16.821238326410324]
  Large language models (LLMs) have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque.<n>Mechanistic interpretability has emerged as a critical research direction for understanding and aligning these models.<n>We analyze how interpretability insights have informed alignment strategies including reinforcement learning from human feedback, constitutional AI, and scalable oversight.
  arXiv  Detail & Related papers  (2026-01-21T11:43:57Z)
• Explainable AI in Big Data Fraud Detection [3.5429848204449694]
  This paper examines how explainable artificial intelligence (XAI) can be integrated into Big Data analytics pipelines for fraud detection and risk management.<n>We review key Big Data characteristics and survey major analytical tools, including distributed storage systems, streaming platforms, and advanced fraud detection models.<n>We identify key research gaps related to scalability, real-time processing, and explainability for graph and temporal models.<n>The paper concludes with open research directions in scalable XAI, privacy-aware explanations, and standardized evaluation methods for explainable fraud detection systems.
  arXiv  Detail & Related papers  (2025-12-17T23:40:54Z)
• Identity Card Presentation Attack Detection: A Systematic Review [7.7489419818764596]
  Deep Learning has driven advances in Presentation Attack Detection.<n>The field is fundamentally limited by a lack of data and the poor generalisation of models.<n>This review consolidates our findings, identifies critical research gaps, and outlines a prescriptive roadmap for future research.
  arXiv  Detail & Related papers  (2025-11-08T15:55:37Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Artificial Behavior Intelligence: Technology, Challenges, and Future Directions [1.5237607855633524]
  This paper defines the technical framework of Artificial Behavior Intelligence (ABI)<n>ABI comprehensively analyzes and interprets human posture, facial expressions, emotions, behavioral sequences, and contextual cues.<n>It details the essential components of ABI, including pose estimation, face and emotion recognition, sequential behavior analysis, and context-aware modeling.
  arXiv  Detail & Related papers  (2025-05-06T08:45:44Z)
• Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications [17.571124565519263]
  In-depth exploration of object detection and semantic segmentation is provided.<n>State-of-the-art advancements in machine learning and deep learning are reviewed.<n>Analysis of big data processing is presented.
  arXiv  Detail & Related papers  (2024-10-21T02:10:49Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Vision Foundation Models in Remote Sensing: A Survey [6.036426846159163]
  Foundation models are large-scale, pre-trained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency.<n>This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for continued development and application of foundation models in remote sensing.
  arXiv  Detail & Related papers  (2024-08-06T22:39:34Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.