Advanced radars and satellites, suitable for remote monitoring, inappropriately reach the economical requirements of short-range detection. Compared with far-sightedness skills, common visible-light sensors offer more ample features conducive to distinguishing the classes. Therefore, ship detection based on visible-light cameras should cooperate with remote detection technologies. However, compared with detectors applied in inland transportation, the lack of fast ship detectors, detecting multiple ship classes, is non-negligible. To fill this gap, we propose a real-time ship detector based on fast U-Net and remapping attention (FRSD) via a common camera. The fast U-Net offered compresses features in the channel dimension to decrease the number of training parameters. The remapping attention introduced boosts the performance in various rain–fog weather conditions while maintaining the real-time speed. The ship dataset proposed contains more than 20,000 samples, alleviating the lack of ship datasets containing various classes. Data augmentation of the cross-background is especially proposed to further promote the diversity of the detecting background. In addition, the rain–fog dataset proposed, containing more than 500 rain–fog images, simulates various marine rain–fog scenarios and soaks the testing image to validate the robustness of ship detectors. Experiments demonstrate that FRSD performs relatively robustly and detects 9 classes with an mAP of more than 83%, reaching a state-of-the-art level.

Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M–1.32 M) and the number of floating-point operations (0.59 G–0.6 G) when compared to recent state-of-the-art methods.

Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing.