Underwater object detection technology is essential for maintaining marine ecological health and supporting economic development. However, the underwater environment poses significant challenges, including low contrast, small object sizes, and complex backgrounds. Existing generic object detectors often fail to identify these organisms effectively. This paper proposes a Joint Multi-scale channel attention and Multi-perception head Network (JMM-Net), a detection algorithm for underwater organisms. JMM-Net comprises three main components: Multi-Scale Channel Attention (MSCA)-based backbone network, Multi-Perception Parallel detection head (MPPhead), and lightweight GSconv-Path Aggregation Network (GS-PAN). MSCA is embedded into the backbone to enhance feature extraction for blurred and small-sized objects in low-quality environments by integrating local and global channel attention through multi-scale parallel sub-networks and cross-channel learning. MPPhead enhances the model’s classification and localization capabilities by leveraging scale, spatial, and task perception, thereby enhancing the detection of marine organisms in complex backgrounds. The adoption of GS-PAN over the traditional Path Aggregation Network (PAN) structure significantly reduces the model’s parameters and computational load, making it more suitable for deployment on edge devices. Extensive experiments on three public underwater datasets demonstrate that our method achieves excellent performance on underwater object detection at a lightweight cost.

Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.