Ship detection is an important but challenging task in the field of computer vision, partially due to the minuscule ship objects in optical remote sensing images and the interference of clouds occlusion and strong waves. Most of the current ship detection methods focus on boosting detection accuracy while they may ignore the detection speed. However, it is also indispensable to increase ship detection speed because it can provide timely ocean rescue and maritime surveillance. To solve the above problems, we propose an improved YOLOv3 (ImYOLOv3) based on attention mechanism, aiming to achieve the best trade-off between detection accuracy and speed. First, to realize high-efficiency ship detection, we adopt the off-the-shelf YOLOv3 as our basic detection framework due to its fast speed. Second, to boost the performance of original YOLOv3 for small ships, we design a novel and lightweight dilated attention module (DAM) to extract discriminative features for ship targets, which can be easily embedded into the basic YOLOv3. The integrated attention mechanism can help our model learn to suppress irrelevant regions while highlighting salient features useful for ship detection task. Furthermore, we introduce a multi-class ship dataset (MSD) and explicitly set supervised subclass according to the scales and moving states of ships. Extensive experiments verify the effectiveness and robustness of ImYOLOv3, and show that our method can accurately detect ships with different scales in different backgrounds, while at a real-time speed.

The development of intelligent transportation systems brings many requirements to the current vehicular networks. For instance, to ensure secure communications between vehicles, low latency, high connectivity, and high data rate are required for vehicular networks. To meet such requirements, the 5G communication systems for vehicular networks should be improved accordingly. This article proposes a communication scheme for 5G vehicular networks, in which moving access points are deployed on vehicles to facilitate the access of vehicle users. Moreover, adjacent vehicle- installed moving access points may cooperatively communicate with vehicle users by joint transmission and reception. In this way, vehicle users communicate with one or more unspecified cooperative access points in a cell-less manner instead of being associated with a single access point. To manage such cell-less networks, local moving software-defined cloudlets are adopted to perform transmission and scheduling management. Simulation results show that the proposed scheme significantly reduces latency while improving the connectivity of vehicular networks, and can be considered as a research direction for the solution to 5G vehicular networks.

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

Cotton foreign fibers directly affect the quality of a textile product; the less foreign fibers in raw cotton, the higher the quality grade of the textile product. Based on the foreign fiber clean machine, this paper proposed an evaluation method of foreign fiber content using deep learning. First of all, a large number of images of foreign fibers were collected from different production lines and annotated to obtain the mask image dataset of foreign fibers. Secondly, by comparing the image segmentation algorithm based on deep learning, tests showed that U-Net has a better performance on different segment metrics evaluations, and U-Net is improved to realize the real-time segmentation of foreign fiber images. The actual size of the foreign fiber could be calculated through the combination of the segment result and the mechanical parameters of the machine. Finally, the test results showed that the relative error between the estimated size and the actual size was less than 4%. After the prototype test, the algorithm was deployed on the actual production line and, by comparing the algorithm data in a random time with the actual foreign fiber statistical data, the overall error was less than 2%. The test showed that the new evaluation method can fully reflect the content of foreign fiber in raw cotton.

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

The purpose of saliency detection is to detect significant regions in the image. Great progress on salient object detection has been made using from deep-learning frameworks. How to effectively extract and integrate multiscale information with different depths is an open problem for salient object detection. In this paper, we propose a processing mechanism based on a balanced attention module and interactive residual module. The mechanism addressed the acquisition of the multiscale features by capturing shallow and deep context information. For effective information fusion, a modified bi-directional propagation strategy was adopted. Finally, we used the fused multiscale information to predict saliency features, which were combined to generate the final saliency maps. The experimental results on five benchmark datasets show that the method is on a par with the state of the art for image saliency datasets, especially on the PASCAL-S datasets, where the MAE reaches 0.092, and on the DUT-OMROM datasets, where the F-measure reaches 0.763.