Polyp segmentation models have recently exhibited considerable success in computer-aided diagnostic systems. Despite the high performance demonstrated by numerous existing deep learning-based techniques on publicly available datasets, these methods still face challenges when it comes to accurate polyp recognition: (1) Undershoot and overshoot problems are frequent. (2) Robustness still needs to be improved in practical application scenarios. To tackle these challenges, we introduce a novel framework called Polyp2Former, which employs a decoupled mask feature strategy. Instead of optimizing the whole region, Polyp2Former divides the mask into the boundary and the body first and utilizes the boundary to refine the final result. It comprises three core components: the Query Embedding Module (QEM), the Mask Decoupling Module (MDM), and the Boundary Guided Module (BGM). These modules collectively contribute to achieving precise and resilient polyp segmentation. In QEM, the framework first embedded boundary and body information as input of MDM and BGM. In the MDM, we first warp the multiscale image features by learning a flow field to make the polyp more consistent, and the resulting body feature and the residual edge feature are further optimized under decoupled supervision by explicitly sampling different parts (polyp or boundary) pixels. In the BGM, we use the boundary map after mapping and the sigmoid function to guide the body feature to predict the final mask with better inner consistency and accurate boundary. Extensive experiments on four challenging polyp semantic segmentation benchmarks demonstrate that our proposed approach improves the segmentation accuracy and robustness significantly against the State-of-the-art methods through five-fold cross-validation and cross-datasets validation.

Underwater robots have been widely used in Marine environment monitoring, deep-sea resources exploration, underwater archaeology, and other fields. The underwater robot hovering is a very demanding technology, especially in a dynamic environment, the underwater multi-disturbance robot has a great influence, and accurate hovering of the underwater robot is the basic guarantee to complete the task. In this paper, a hybrid control strategy based on a neural network and PID is proposed to realize the perception and decision of complex environment states and realize the accurate hovering of the underwater robot. Experimental results show that the hybrid control based on neural network and PID can stably and accurately complete the hovering function, which proves the effectiveness of the algorithm. (Video: https://youtu.be/1GU4BKHeTB8t)

This article proposes a modified rapidly exploring random tree (RRT)-based path planner and behavior-based cooperative tracking strategy for a dual robotic dolphin system to fulfill a cooperative target-tracking task. Specifically, with full consideration of both task requirements and mechatronic configuration, a robotic dolphin with a waist-caudal propulsive mechanism for thrust forces and differential bilateral flippers for maneuverability is developed. To satisfy the demand of fast path planning, a variant RRT algorithm is employed to generate feasible paths for the dual robotic dolphins with fewer waypoints, faster convergence speed, and better stability. Furthermore, a behavior-based approach in conjunction with centralized architecture is implemented to achieve high-level decision-making. Finally, simulations, analysis, as well as field experiments are carried out to verify the effectiveness of the proposed control scheme. The success of the experiments further offers insight into the mechanisms of cooperative multirobot target tracking in aquatic environments.