Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
- URL: http://arxiv.org/abs/2408.02924v1
- Date: Tue, 6 Aug 2024 03:20:10 GMT
- Title: Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
- Authors: Shijie Lian, Hua Li,
- Abstract summary: The Segment Anything Model (SAM) and its extensions have been attempted for applications in various underwater visualization tasks in marine sciences.
Recently, Meta has developed the Segment Anything Model 2 (SAM2), which significantly improves running speed and segmentation accuracy.
This report aims to explore the potential of SAM2 in marine science by evaluating it on the underwater instance segmentation datasets benchmark UIIS and USIS10K.
- Score: 2.0554501265326794
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With breakthroughs in large-scale modeling, the Segment Anything Model (SAM) and its extensions have been attempted for applications in various underwater visualization tasks in marine sciences, and have had a significant impact on the academic community. Recently, Meta has further developed the Segment Anything Model 2 (SAM2), which significantly improves running speed and segmentation accuracy compared to its predecessor. This report aims to explore the potential of SAM2 in marine science by evaluating it on the underwater instance segmentation benchmark datasets UIIS and USIS10K. The experiments show that the performance of SAM2 is extremely dependent on the type of user-provided prompts. When using the ground truth bounding box as prompt, SAM2 performed excellently in the underwater instance segmentation domain. However, when running in automatic mode, SAM2's ability with point prompts to sense and segment underwater instances is significantly degraded. It is hoped that this paper will inspire researchers to further explore the SAM model family in the underwater domain. The results and evaluation codes in this paper are available at https://github.com/LiamLian0727/UnderwaterSAM2Eval.
Related papers
- Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes [63.966251473172036]
The foundational model SAM has influenced multiple fields within computer vision, and its upgraded version, SAM 2, enhances capabilities in video segmentation.
While SAMs have demonstrated excellent performance in segmenting context-independent concepts like people, cars, and roads, they overlook more challenging context-dependent (CD) concepts, such as visual saliency, camouflage, product defects, and medical lesions.
We conduct a thorough quantitative evaluation of SAMs on 11 CD concepts across 2D and 3D images and videos in various visual modalities within natural, medical, and industrial scenes.
arXiv Detail & Related papers (2024-12-02T08:03:56Z) - Det-SAM2:Technical Report on the Self-Prompting Segmentation Framework Based on Segment Anything Model 2 [0.0]
This report focuses on the construction of the overall Det-SAM2 framework and the subsequent engineering optimization applied to SAM2.
We present a case demonstrating an application built on the Det-SAM2 framework: AI refereeing in a billiards scenario, derived from our business context.
arXiv Detail & Related papers (2024-11-28T07:58:30Z) - Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation [2.5524809198548137]
Segment Anything Model (SAM) has demonstrated powerful zero-shot segmentation performance in natural scenes.
Recently released Segment Anything Model 2 (SAM2) has further heightened researchers' expectations towards image segmentation capabilities.
This technique report can drive the emergence of SAM2-based adapters, aiming to enhance the performance ceiling of large vision models on class-agnostic instance segmentation tasks.
arXiv Detail & Related papers (2024-09-04T09:35:09Z) - SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation [51.90445260276897]
We prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models.
We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation.
arXiv Detail & Related papers (2024-08-16T17:55:38Z) - SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation [13.609341065893739]
This study explores the zero-shot segmentation performance of SAM 2 in robot-assisted surgery based on prompts.
We employ two forms of prompts: 1-point and bounding box, while for video sequences, the 1-point prompt is applied to the initial frame.
The results with point prompts also exhibit a substantial enhancement over SAM's capabilities, nearing or even surpassing existing unprompted SOTA methods.
arXiv Detail & Related papers (2024-08-08T17:08:57Z) - Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2 [10.751277821864916]
Report reveals a decline in SAM2's ability to perceive different objects in images without prompts in its auto mode.
Specifically, we employ the challenging task of camouflaged object detection to assess this performance decrease.
arXiv Detail & Related papers (2024-07-31T13:32:10Z) - Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset [60.14089302022989]
Underwater vision tasks often suffer from low segmentation accuracy due to the complex underwater circumstances.
We construct the first large-scale underwater salient instance segmentation dataset (USIS10K)
We propose an Underwater Salient Instance architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain.
arXiv Detail & Related papers (2024-06-10T06:17:33Z) - MAS-SAM: Segment Any Marine Animal with Aggregated Features [55.91291540810978]
We propose a novel feature learning framework named MAS-SAM for marine animal segmentation.
Our method enables to extract richer marine information from global contextual cues to fine-grained local details.
arXiv Detail & Related papers (2024-04-24T07:38:14Z) - Moving Object Segmentation: All You Need Is SAM (and Flow) [82.78026782967959]
We investigate two models for combining SAM with optical flow that harness the segmentation power of SAM with the ability of flow to discover and group moving objects.
In the first model, we adapt SAM to take optical flow, rather than RGB, as an input. In the second, SAM takes RGB as an input, and flow is used as a segmentation prompt.
These surprisingly simple methods, without any further modifications, outperform all previous approaches by a considerable margin in both single and multi-object benchmarks.
arXiv Detail & Related papers (2024-04-18T17:59:53Z) - Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM [62.85895749882285]
Marine Animal (MAS) involves segmenting animals within marine environments.
We propose a novel feature learning framework, named Dual-SAM for high-performance MAS.
Our proposed method achieves state-of-the-art performances on five widely-used MAS datasets.
arXiv Detail & Related papers (2024-04-07T15:34:40Z)
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.