Automated marine oil spill detection algorithm based on single-image generative adversarial network and YOLO-v8 under small samples.

As marine resources and transportation develop, oil spill incidents are increasing, endangering marine ecosystems and human lives. Rapidly and accurately identifying marine oil spill is of utmost importance in protecting marine ecosystems. Marine oil spill detection methods based on deep learning and computer vision have the great potential significantly enhance detection efficiency and accuracy, but their performance is often limited by the scarcity of real oil spill samples, posing a challenging to train a precise detection model. This study introduces a detection method specifically designed for scenarios with limited sample sizes. First, the small sample dataset of marine oil spill taken by Landsat-8 satellite is used as the training set. Then, a single image generative adversarial network (SinGAN) capable of training with a single oil spill image is constructed for expanding the dataset, generating diverse marine oil spill samples with different shapes. Second, a YOLO-v8 model is pretrained via the method of transfer learning and then trained with dataset before and after augmentation separately for real-time and efficient oil spill detection. Experimental results have demonstrated that the YOLO-v8 model, trained on an expanded dataset, exhibits notable enhancements in recall, precision, and average precision, with improvements of 12.3 %, 6.3 %, and 11.3 % respectively, compared to the unexpanded dataset. It reveals that our marine oil spill detection model based on YOLO-v8 exhibits leading or comparable performance in terms of recall, precision, and AP metrics. The data augmentation technique based on SinGAN contributes to the performance of other popular object detection algorithms as well.

Event-triggered adaptive sliding mode control for consensus of multiagent systems with unknown disturbances.

In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.