As marine observation technology develops rapidly, underwater optical image object detection is beginning to occupy an important role in many tasks, such as naval coastal defense tasks, aquaculture, etc. However, in the complex marine environment, the images captured by an optical imaging system are usually severely degraded. Therefore, how to detect objects accurately and quickly under such conditions is a critical problem that needs to be solved. In this manuscript, a novel framework for underwater object detection based on a hybrid transformer network is proposed. First, a lightweight hybrid transformer-based network is presented that can extract global contextual information. Second, a fine-grained feature pyramid network is used to overcome the issues of feeble signal disappearance. Third, the test-time-augmentation method is applied for inference without introducing additional parameters. Extensive experiments have shown that the approach we have proposed is able to detect feeble and small objects in an efficient and effective way. Furthermore, our model significantly outperforms the latest advanced detectors with respect to both the number of parameters and the mAP by a considerable margin. Specifically, our detector outperforms the baseline model by 6.3 points, and the model parameters are reduced by 28.5 M.

Underwater optical image object detection plays a crucial role in fields such as ocean exploration. However, constructing a comprehensive annotated dataset for training is challenging, especially when dealing with severely degraded underwater imagery. The sparsity of annotations can significantly reduce the performance of object detection algorithms. Existing methods designed for sparsely annotated object detection in terrestrial scenarios are not optimal for underwater conditions. To address these challenges, we propose a novel underwater cooperative training framework. Specifically, we propose a novel conjugate data generation module to tackle the issue of noise accumulation inherent in the existing data generation module, thereby greatly enhancing pseudo label generation. Furthermore, to mitigate the impacts of noisy pseudo labels, we present a pseudo label calibration strategy that manipulates the foreground confidence trend towards a low entropy distribution, effectively eliminating noisy pseudo labels. Finally, we propose a novel decoupled detection module to alleviate interference between position information and foreground confidence, further reducing noisy pseudo labels. Compared with methods tailored for terrestrial conditions with sparse annotations, our approach demonstrates superior performance in underwater scenarios. We conducted extensive experiments on various underwater datasets, including URPC2018 and DUO etc. The results show that our method outperforms the existing state-of-the-art by 2.0 mean Average Precision (mAP) on the URPC2018 and 0.9 mAP on the DUO datasets, while achieving state-of-the-art performance. Code will be released at https://github.com/bobchenlut/coorporate-learing.

Object detection in underwater optical imagery plays a crucial role in various fields related to underwater exploration. However, manual annotation of such images often results in incomplete ground truth due to its severe degradation. In this study, we address the issue of incomplete supervision signals in degraded underwater images by reframing it as a sparse annotation challenge. Specifically, we present a novel method for object detection in sparsely annotated underwater scenarios. Our approach involves an effective pseudo-label generation network designed to produce labels for the degraded foreground lacking annotations. To mitigate potential background noise resulting from the discrepancy between the fixed confidence threshold and its dynamic distribution, we introduce a novel dynamic adaptive confidence threshold method. Additionally, a novel adaptive geometric prior-based noise reduction strategy is designed to eliminate noisy pseudo-labels with low-quality localization. We validate and analyze our approach through experiments on publicly available underwater optical image datasets. The results demonstrate that our approach achieves significant performance improvements across various sparsity conditions. Compared with existing state-of-the-art models, our proposed approach delivers significantly superior Average Precision (AP) performance while maintaining fast inference speeds. The code is available at https://github.com/chenyyyxxx/Underwater-aisi.

Maritime recycling plays a significant role in reusing components from spacecraft. However, the current methods for detecting and recovering sunken spacecraft wrecks suffer from inaccurate detection and low efficiency. To improve the detection effectiveness of rocket wrecks, this paper proposes a method based on underwater optical images which leverage the high-resolution features of images. To address the issue of low accuracy and efficiency in detecting rocket wrecks, a lightweight deep neural network detection model is introduced to enhance accuracy while reducing the computational load and increasing efficiency. Finally, through quantitative and qualitative analyses, our proposed method achieves a detection accuracy of 96.0% with low computational power, which lays a solid foundation for spacecraft recycling.