SAR images have become the latest high-resolution imaging equipment, which can monitor the Earth 24 hours a day. More and more deep learning technologies are applied to ship target detection, however, in complex environments, due to the small target of the ship, problems such as false detection and miss detection often occur. For this reason, SSPNet is proposed with several small-target-augmentation strategies to complete the detection of small ships on the sea. This network is an improvement of FPN. The model uses Context Attention Module (CAM), Scale Enhancement Module (SEM), and Scale Selection Module (SSM). CAM introduces the attention heat map, SEM uses the residual module to make the network pay more attention to specific scale targets, SSM introduces deep semantic features into shallow features. Weighted Negative Sampling (WNS) strategy is proposed to enable the network to select more representative samples. These modules make the network more suitable for small target detection. The results on SSDD dataset show that the model is superior to the existing object detection network, and the AP50 reaches 91.57%.

Considering the efficiency and security of healthcare data processing, indiscriminate data collection, annotation, and transmission are unwise. In this article, we propose the normalized double entropy (NDE) method to assess image data quality in the form of metatask. In specific, the probability entropy and distance entropy are both adopted and normalized to evaluate the data quality. The experimental results show the stable ability of the NDE to distinguish good and bad data in terms of information contribution. Furthermore, the model's diagnostic performances driven by selected good and bad data are compared, and a clear gap exists between them under the premise of the same amount of data. Screening 70% of the dataset can achieve almost the same accuracy as that based on all data. This article focuses on healthcare data quality and data redundancy and provides a practical evaluation tool to facilitate the identification and collection of valuable data, which is beneficial to improve efficiency and protect cybersecurity in healthcare systems.

Human agricultural activities are always accompanied by pests and diseases, which have brought great losses to the production of crops. Intelligent algorithms based on deep learning have achieved some achievements in the field of pest control, but relying on a large amount of data to drive consumes a lot of resources, which is not conducive to the sustainable development of smart agriculture. The research in this paper starts with data, and is committed to finding efficient data, solving the data dilemma, and helping sustainable agricultural development. Starting from the data, this paper proposed an Edge Distance-Entropy data evaluation method, which can be used to obtain efficient crop pests, and the data consumption is reduced by 5% to 15% compared with the existing methods. The experimental results demonstrate that this method can obtain efficient crop pest data, and only use about 60% of the data to achieve 100% effect. Compared with other data evaluation methods, the method proposed in this paper achieve state-of-the-art results. The work conducted in this paper solves the dilemma of the existing intelligent algorithms for pest control relying on a large amount of data, and has important practical significance for realizing the sustainable development of modern smart agriculture.

In many fields, due to the lack of large-scale training data, the traditional object detection methods cannot complete the actual work well. The main reason is the overfitting problem and lack of the generalization ability. In this work, we propose a general method to alleviate the overfitting problem in the few-shot object detection. Our work extends Faster R-CNN with self-knowledge distillation algorithm and designs the loss function with attention mechanism, which can improve true detection in the foreground. In this way, object detector can learn an approximate mapping relationship from few samples, which makes the network possess a stronger generalization ability when tackling few images. Through numerous comparative experiments, we demonstrate that our method is general and feasible on VOC and COCO benchmarks datasets with different settings. We provide a new idea for solving the problem of few-shot object detection, and produce an excellent performance of recall rate on few-shot object detection.