Our paper has been published in Pattern Recognition.
- For future researchers to develop better algorithms in the field of underwater object detection, we upload all the detection results, the download link is https://pan.baidu.com/s/1UMQx1qjoSZ9X8d_EGnE1rQ （Code: adrh). We plot the ground truth and detection results on one image. From these test results, you can understand the advantages and weaknesses of our method. Welcome New Work on UOD!
- We upload the test model of the proposed method, you can reproduce the results of our paper. The download link is https://pan.baidu.com/s/1avBWJJm2sTLPoz9tl0eKWw (Code: aert).
A Gated Cross-domain Collaborative Network for Underwater Object Detection (https://arxiv.org/pdf/2306.14141.pdf).
Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light conditions in underwater environments, several underwater image enhancement (UIE) methods have been proposed to improve the quality of underwater images. However, only using the enhanced images does not improve the performance of UOD, since it may unavoidably remove or alter critical patterns and details of underwater objects. In contrast, we believe that exploring the complementary information from the two domains is beneficial for UOD. The raw image preserves the natural characteristics of the scene and texture information of the objects, while the enhanced image improves the visibility of underwater objects. Based on this perspective, we propose a Gated Cross-domain Collaborative Network (GCC-Net) to address the challenges of poor visibility and low contrast in underwater environments, which comprises three dedicated components. Firstly, a real-time UIE method is employed to generate enhanced images, which can improve the visibility of objects in low-contrast areas. Secondly, a cross-domain feature interaction module is introduced to facilitate the interaction and mine complementary information between raw and enhanced image features. Thirdly, to prevent the contamination of unreliable generated results, a gated feature fusion module is proposed to adaptively control the fusion ratio of cross-domain information. Our method presents a new UOD paradigm from the perspective of cross-domain information interaction and fusion. Experimental results demonstrate that the proposed GCC-Net achieves state-of-the-art performance on four underwater datasets.
(1) DUO dataset (https://github.com/chongweiliu/DUO)
(2) Trashcan dataset (https://conservancy.umn.edu/handle/11299/214865)
(3) WPBB dataset (https://github.com/fedezocco/MoreEffEffDetsAndWPBB-TensorFlow/tree/main/WPBB_dataset)
(4) Brackish dataset (https://www.kaggle.com/datasets/aalborguniversity/brackish-dataset)
for example, the path in 'configs'.
CUDA_VISIBLE_DEVICES=2,3 ./tools/dist_train.sh configs/autoassign/autoassign_r50_fpn_8x2_3x_gcc_duo.py 2
CUDA_VISIBLE_DEVICES=2,3 ./tools/dist_train.sh configs/autoassign/autoassign_r50_fpn_8x2_3x_gcc_trashcan.py 2
CUDA_VISIBLE_DEVICES=2,3 ./tools/dist_train.sh configs/autoassign/autoassign_r50_fpn_8x2_3x_gcc_wpbb.py 2
CUDA_VISIBLE_DEVICES=2,3 ./tools/dist_train.sh configs/autoassign/autoassign_r50_fpn_8x2_3x_gcc_wpbb.py 2
For main results, I have provided the currently trained weight file, which can be downloaded directly for testing.
CUDA_VISIBLE_DEVICES=2 python tools/test.py configs/autoassign/autoassign_r50_fpn_8x2_3x_gcc_duo.py weights/gcc_net_epoch_36.pth --eval bbox
Please refer to install.md for installation guide.
Please see get_started.md for the basic usage of MMDetection.
MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors