Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study
Underwater image enhancement is often perceived as a disadvantageous process to object detection. We propose a novel analysis of the interactions between enhancement and detection, elaborating on the potential of enhancement to improve detection. In particular, we evaluate object detection performan...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/185 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587573018492928 |
|---|---|
| author | Ashraf Saleem Ali Awad Sidike Paheding Evan Lucas Timothy C. Havens Peter C. Esselman |
| author_facet | Ashraf Saleem Ali Awad Sidike Paheding Evan Lucas Timothy C. Havens Peter C. Esselman |
| author_sort | Ashraf Saleem |
| collection | DOAJ |
| description | Underwater image enhancement is often perceived as a disadvantageous process to object detection. We propose a novel analysis of the interactions between enhancement and detection, elaborating on the potential of enhancement to improve detection. In particular, we evaluate object detection performance for each individual image rather than across the entire set to allow a direct performance comparison of each image before and after enhancement. This approach enables the generation of unique queries to identify the outperforming and underperforming enhanced images compared to the original images. To accomplish this, we first produce enhanced image sets of the original images using recent image enhancement models. Each enhanced set is then divided into two groups: (1) images that outperform or match the performance of the original images and (2) images that underperform. Subsequently, we create mixed original-enhanced sets by replacing underperforming enhanced images with their corresponding original images. Next, we conduct a detailed analysis by evaluating all generated groups for quality and detection performance attributes. Finally, we perform an overlap analysis between the generated enhanced sets to identify cases where the enhanced images of different enhancement algorithms unanimously outperform, equally perform, or underperform the original images. Our analysis reveals that, when evaluated individually, most enhanced images achieve equal or superior performance compared to their original counterparts. The proposed method uncovers variations in detection performance that are not apparent in a whole set as opposed to a per-image evaluation because the latter reveals that only a small percentage of enhanced images cause an overall negative impact on detection. We also find that over-enhancement may lead to deteriorated object detection performance. Lastly, we note that enhanced images reveal hidden objects that were not annotated due to the low visibility of the original images. |
| format | Article |
| id | doaj-art-636598d64aa44756af2b6be06ff18406 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-636598d64aa44756af2b6be06ff184062025-01-24T13:47:39ZengMDPI AGRemote Sensing2072-42922025-01-0117218510.3390/rs17020185Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative StudyAshraf Saleem0Ali Awad1Sidike Paheding2Evan Lucas3Timothy C. Havens4Peter C. Esselman5Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USADepartment of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI 49931, USADepartment of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USAInstitute of Computing and Cybersystems, Michigan Technological University, Houghton, MI 49931, USADepartment of Computer Science, College of Computing, Michigan Technological University, Houghton, MI 49931, USAU.S. Geological Survey Great Lakes Science Center, Ann Arbor, MI 48105, USAUnderwater image enhancement is often perceived as a disadvantageous process to object detection. We propose a novel analysis of the interactions between enhancement and detection, elaborating on the potential of enhancement to improve detection. In particular, we evaluate object detection performance for each individual image rather than across the entire set to allow a direct performance comparison of each image before and after enhancement. This approach enables the generation of unique queries to identify the outperforming and underperforming enhanced images compared to the original images. To accomplish this, we first produce enhanced image sets of the original images using recent image enhancement models. Each enhanced set is then divided into two groups: (1) images that outperform or match the performance of the original images and (2) images that underperform. Subsequently, we create mixed original-enhanced sets by replacing underperforming enhanced images with their corresponding original images. Next, we conduct a detailed analysis by evaluating all generated groups for quality and detection performance attributes. Finally, we perform an overlap analysis between the generated enhanced sets to identify cases where the enhanced images of different enhancement algorithms unanimously outperform, equally perform, or underperform the original images. Our analysis reveals that, when evaluated individually, most enhanced images achieve equal or superior performance compared to their original counterparts. The proposed method uncovers variations in detection performance that are not apparent in a whole set as opposed to a per-image evaluation because the latter reveals that only a small percentage of enhanced images cause an overall negative impact on detection. We also find that over-enhancement may lead to deteriorated object detection performance. Lastly, we note that enhanced images reveal hidden objects that were not annotated due to the low visibility of the original images.https://www.mdpi.com/2072-4292/17/2/185underwater image enhancementunderwater object detectionunderwater dataset |
| spellingShingle | Ashraf Saleem Ali Awad Sidike Paheding Evan Lucas Timothy C. Havens Peter C. Esselman Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study Remote Sensing underwater image enhancement underwater object detection underwater dataset |
| title | Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study |
| title_full | Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study |
| title_fullStr | Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study |
| title_full_unstemmed | Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study |
| title_short | Understanding the Influence of Image Enhancement on Underwater Object Detection: A Quantitative and Qualitative Study |
| title_sort | understanding the influence of image enhancement on underwater object detection a quantitative and qualitative study |
| topic | underwater image enhancement underwater object detection underwater dataset |
| url | https://www.mdpi.com/2072-4292/17/2/185 |
| work_keys_str_mv | AT ashrafsaleem understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy AT aliawad understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy AT sidikepaheding understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy AT evanlucas understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy AT timothychavens understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy AT petercesselman understandingtheinfluenceofimageenhancementonunderwaterobjectdetectionaquantitativeandqualitativestudy |