YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships
The accurate detection of small ships based on images or vision is critical for many scenarios, like maritime surveillance, port security, and navigation safety. However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirem...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/5/925 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849711816877277184 |
|---|---|
| author | Liran Shen Tianchun Gao Qingbo Yin |
| author_facet | Liran Shen Tianchun Gao Qingbo Yin |
| author_sort | Liran Shen |
| collection | DOAJ |
| description | The accurate detection of small ships based on images or vision is critical for many scenarios, like maritime surveillance, port security, and navigation safety. However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirement, they have unacceptable computation costs for real-time surveillance. We propose YOLO-LPSS, a novel model designed to significantly improve small ship detection accuracy with low computation cost. The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. The experimental results show that YOLO-LPSS outperforms the known YOLOv8 nano baseline and other works, and the number of parameters increases by only 0.33 M compared to the original YOLOv8n while achieving 0.796 and 0.831 AP<sup>50:95</sup> in classes consisting mainly of small ship targets (the bounding box of the target area is less than 5% of the image resolution), which is 3–5% higher than the vanilla model and recent SOTA models. |
| format | Article |
| id | doaj-art-315e787d07694fdfa001f51d7dc0f56f |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-315e787d07694fdfa001f51d7dc0f56f2025-08-20T03:14:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113592510.3390/jmse13050925YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea ShipsLiran Shen0Tianchun Gao1Qingbo Yin2College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116023, ChinaCollege of Marine Electrical Engineering, Dalian Maritime University, Dalian 116023, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian 116023, ChinaThe accurate detection of small ships based on images or vision is critical for many scenarios, like maritime surveillance, port security, and navigation safety. However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirement, they have unacceptable computation costs for real-time surveillance. We propose YOLO-LPSS, a novel model designed to significantly improve small ship detection accuracy with low computation cost. The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. The experimental results show that YOLO-LPSS outperforms the known YOLOv8 nano baseline and other works, and the number of parameters increases by only 0.33 M compared to the original YOLOv8n while achieving 0.796 and 0.831 AP<sup>50:95</sup> in classes consisting mainly of small ship targets (the bounding box of the target area is less than 5% of the image resolution), which is 3–5% higher than the vanilla model and recent SOTA models.https://www.mdpi.com/2077-1312/13/5/925small ship detectiondeep learningdynamic up-samplelightweight model |
| spellingShingle | Liran Shen Tianchun Gao Qingbo Yin YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships Journal of Marine Science and Engineering small ship detection deep learning dynamic up-sample lightweight model |
| title | YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships |
| title_full | YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships |
| title_fullStr | YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships |
| title_full_unstemmed | YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships |
| title_short | YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships |
| title_sort | yolo lpss a lightweight and precise detection model for small sea ships |
| topic | small ship detection deep learning dynamic up-sample lightweight model |
| url | https://www.mdpi.com/2077-1312/13/5/925 |
| work_keys_str_mv | AT liranshen yololpssalightweightandprecisedetectionmodelforsmallseaships AT tianchungao yololpssalightweightandprecisedetectionmodelforsmallseaships AT qingboyin yololpssalightweightandprecisedetectionmodelforsmallseaships |