A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection
Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/214 |
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| _version_ | 1841548931189702656 |
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| author | Maria Trigka Elias Dritsas |
| author_facet | Maria Trigka Elias Dritsas |
| author_sort | Maria Trigka |
| collection | DOAJ |
| description | Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications. |
| format | Article |
| id | doaj-art-864371567b7b462c9ed4d4f1aaf5f34d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-864371567b7b462c9ed4d4f1aaf5f34d2025-01-10T13:21:15ZengMDPI AGSensors1424-82202025-01-0125121410.3390/s25010214A Comprehensive Survey of Machine Learning Techniques and Models for Object DetectionMaria Trigka0Elias Dritsas1Industrial Systems Institute, Athena Research and Innovation Center, 26504 Patras, GreeceIndustrial Systems Institute, Athena Research and Innovation Center, 26504 Patras, GreeceObject detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications.https://www.mdpi.com/1424-8220/25/1/214object detectionmachine learningdeep learningtechniquesmodels |
| spellingShingle | Maria Trigka Elias Dritsas A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection Sensors object detection machine learning deep learning techniques models |
| title | A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection |
| title_full | A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection |
| title_fullStr | A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection |
| title_full_unstemmed | A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection |
| title_short | A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection |
| title_sort | comprehensive survey of machine learning techniques and models for object detection |
| topic | object detection machine learning deep learning techniques models |
| url | https://www.mdpi.com/1424-8220/25/1/214 |
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