Recently, ship detection technology has been applied extensively in the marine security monitoring field. However, achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales, slightly occluded objects, uneven illumination, and sea clutter. To address these issues, we propose a novel ship detection approach, i.e., the Twin Feature Pyramid Network and Data Augmentation (TFPN-DA), which mainly consists of three modules. First, to eliminate the negative effects of slightly occluded objects and uneven illumination, we propose the Spatial Attention within the Twin Feature Pyramid Network (SA-TFPN) method, which is based on spatial attention to reconstruct the feature pyramid. Second, the ROI Feature Module (ROIFM) is introduced into the SA-TFPN, which is used to enhance specific crucial details from multi-scale features for object regression and classification. Additionally, data augmentation strategies such as spatial affine transformation and noise processing, are developed to optimize the data sample distribution. A self-construct dataset is used to train the detection model, and the experiments conducted on the dataset demonstrate the effectiveness of our model.

Self-adaptive systems are able to adjust their behaviour in response to environmental condition changes and are widely deployed as Internetwares. Considered as a promising way to handle the ever-growing complexity of software systems, they have seen an increasing level of interest and are covering a variety of applications, e.g., autonomous car systems and adaptive network systems. Many approaches for the construction of self-adaptive systems have been developed, and probabilistic models, such as Markov decision processes (MDPs), are one of the favoured. However, the majority of them do not deal with the problems of the underlying MDP being obsolete under new environments or unsatisfactory to the given properties. This results in the generated policies from such MDP failing to guide the self-adaptive system to run correctly and meet goals. In this article, we propose a systematic approach to updating an obsolete MDP by exploring new states and transitions and removing obsolete ones, and repairing an unsatisfactory MDP by adjusting its structure in a more meaningful way rather than arbitrarily changing the transition probabilities to values not in line with reality. Experimental results show that the MDPs updated and repaired by our approach are more competent in guiding the self-adaptive systems' correct running compared with the original ones.