International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Survey on Object Detection Techniques for Foggy Images Namrata Deshmukh1, Anuradha Purohit2 1M.Tech Scholar, Dept. of Computer Engineering, SGSITS, M.P., India 2Professor, Dept. of Computer Engineering, SGSITS, M.P., India
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Abstract - Object detection under unfavorable weather
visual quality by concealing details, decreasing contrast, and producing color distortion. This poses issues for the application of object detection algorithms, highlighting the need for additional investigation and solution. Fog affects visibility and image quality, making images hazy, dull, and blocked, reducing the accuracy, and efficiency of objects detecting models [5], [6]. Generally, to identify objects in hazy weather, both de-hazed methods and object detectors are combined to achieve this goal [7]. Researchers working on this domain came up with various innovative detection methods, which also lead to new problems/issues. In this survey paper, we try to answer the call for more robust object detection when it comes to operating in these environments. While general object detection has been studied extensively, the specific challenge of adverse conditions such as fog is less well-known [4]. This paper describes the full flow for object detection in foggy images from pre-processing and image enhancement to feature extraction and detection model. This contributes previous work by providing elaborative work done in object detection especially in foggy images [7].
conditions, particularly in foggy environments, presents significant challenges due to reduced visibility and image degradation. Fog and clouds frequently hides objects in imagery captured by drones, especially in the early morning, making accurate detection difficult. Despite the rapid advancements in object detection algorithms across various domains, including autonomous vehicles, healthcare, and surveillance, achieving high performance in foggy conditions remains a complex task. This paper presents a survey on object detection techniques for foggy images, systematically investigating the workflow and covering critical stages such as data collection, pre-processing, image enhancement, feature extraction, object detection, and evaluation. Special emphasis is placed on advanced image enhancement and feature extraction techniques to improve object visibility. Furthermore, a comparative analysis of different methodologies, datasets, and performance metrics is presented through detailed tables and visualizations. This research not only highlights current challenges but also identifies potential directions for future advancements in object detection under foggy conditions, providing valuable insights for researchers and practitioners alike.
This study’s main contributions include the following: 1) Pre-processing and improvement approaches, including an overview, algorithm descriptions, and practical implementation under hazy conditions.
Key Words: Object Detection, Foggy Images, Image Enhancement, Feature Extraction, Deep Learning, CNN.
1. INTRODUCTION
2) In-depth analysis of image enhancement and feature extraction techniques and their adaptations for foggy environments.
Object detection plays a pivotal role in a wide range of computer vision applications. It involves correctly categorizing and locating things in a given image using a rectangular bounding box [1]. It finds broad applicability across domains such as autonomous vehicles, surveillance systems, aviation safety, industrial automation, robotics, and other autonomous technologies. Object detection approaches are quickly changing with advances in convolution neural networks, deep learning algorithms, and GPU computing power [2]. While standard models can identify objects effectively, doing so in foggy environments presents significant challenges [3]. Adverse weather condition like rain, fog, snow and cloud degrade the functioning of camera sensors and reduce visibility. Such that, tasks like classification, detection and segmentation works well in daylight because of high visibility but their performance gradually decreases due to adverse weather condition, Since real-world scenarios often involve rapid weather changes. [4]. In foggy weather conditions, flying particles can reduce
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3) Provide a list of areas of improvement with possible research directions to inspirations for other studies. This survey aims to be a fundamental reference tool for researchers, students, and practitioners by providing an extensive road map of well-established methods highlighting potential opportunities for innovation. This work aims to develop effective object identification algorithms in adverse weather, opening up new opportunities for innovation in the space. In this survey paper, we studied and analyzed research papers published between 2006 and 2024 by reputable publishers such as IEEE, ACM, Springer, and other renowned journals and conferences. The remainder of this paper is organized as follows: Section II reviews the background and related work; Section III outlines the challenges posed by fog; Section IV describes the dataset used; Section V details the proposed methodology; Section VI
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