In this paper, we propose an underwater object detection method using monocular vision sensors. In addition to commonly used visual features such as color and intensity, we investigate the potential of underwater object detection using light transmission information. The global contrast of various features is used to initially identify the region of interest (ROI), which is then filtered by the image segmentation method, producing the final underwater object detection results. We test the performance of our method with diverse underwater datasets. Samples of the datasets are acquired by a monocular camera with different qualities (such as resolution and focal length) and setups (viewing distance, viewing angle, and optical environment). It is demonstrated that our ROI detection method is necessary and can largely remove the background noise and significantly increase the accuracy of our underwater object detection method.

Industrial Internet of Things (IIoT) provides significant support for observing and controlling industrial machinery. In this paper, a novel hierarchical feature enhancement network (HFEN) is proposed by combining signal processing and representation learning. The signal processing is based on frequency and time frequency analysis, which extracts features with definite physical significance, whereas representation learning improves the representability of the physical features by connecting stacked denoising autoencoders and squeeze-and-excitation networks. A novel two-stream architecture is designed for HFEN to fuse two types of features. Consequently, HFEN can extract features that can be analyzed for physical significance and that are also representative in terms of recognizable patterns. The experimental results prove that the performance of HFEN is satisfactory in terms of accuracy and efficiency when compared to other methods. Finally, this study also aims to demonstrate the potential of a new pairing that fuses the model- and data-driven strategies for IIoT.

Rotating machinery is ubiquitous, and its failures constitute a major cause of the failures of transportation infrastructures. Most fault-diagnosis methods for rotating machinery are based on vibration-signal analysis because vibrations directly reflect the transient regime of machinery elements. This article proposes a novel multiscale shared-learning network (MSSLN) architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. The architecture fuses layer-wise activations with multiscale flows, to enable the network to fully learn the shared representation with consistency across multiscale factors. This characteristic helps MSSLN provide more faithful diagnoses than existing single- and multiscale methods. Experiments on bearing and gearbox datasets are used to evaluate the fault-diagnosis performance of transportation infrastructures. Extensive experimental results and comprehensive analyses demonstrate the superiority of the proposed MSSLN in fault diagnosis for bearings and gearboxes, the two foundational elements in transportation infrastructures.

Underwater optical environments are seriously affected by various optical inputs, such as artificial light, sky light, and ambient scattered light. The latter two can block underwater object segmentation tasks, since they inhibit the emergence of objects of interest and distort image information, while artificial light can contribute to segmentation. Artificial light often focuses on the object of interest, and, therefore, we can initially identify the region of target objects if the collimation of artificial light is recognized. Based on this concept, we propose an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light as a candidate object region. Then, the second phase employs a level set method to segment the objects of interest within the candidate region. This two-phase structure largely removes background noise and highlights the outline of underwater objects. We test the performance of the method with diverse underwater datasets, demonstrating that it outperforms previous methods.

Maritime search and rescue is a crucial component of the national emergency response system, which mainly relies on unmanned aerial vehicles (UAVs) to detect objects. Most traditional object detection methods focus on boosting the detection accuracy while neglecting the detection speed of the heavy model. However, improving the detection speed is essential, which can provide timely maritime search and rescue. To address the issues, we propose a lightweight object detector named Shuffle-GhostNet-based detector (SG-Det). First, we construct a lightweight backbone named Shuffle-GhostNet, which enhances the information flow between channel groups by redesigning the correlation group convolution and introducing the channel shuffle operation. Second, we propose an improved feature pyramid model, namely BiFPN-tiny, which has a lighter structure capable of reinforcing small object features. Furthermore, we incorporate the Atrous Spatial Pyramid Pooling module (ASPP) into the network, which employs atrous convolution with different sampling rates to obtain multi-scale information. Finally, we generate three sets of bounding boxes at different scales—large, medium, and small—to detect objects of different sizes. Compared with other lightweight detectors, SG-Det achieves better tradeoffs across performance metrics and enables real-time detection with an accuracy rate of over 90% for maritime objects, showing that it can better meet the actual requirements of maritime search and rescue.