Human Action Recognition (HAR) based on skeleton sequences has attracted much attention due to the robustness and background insensitivity of skeletal data. The convolutional neural network (CNN) for spatio-temporal representation learning has been widely utilized for skeleton-based HAR. However, the long-term spatio-temporal modeling and action category-specific feature attention have not been fully exploited. In order to explore the current potential of CNNs for skeleton-based HAR, a novel CNN architecture with temporal-channel attention and convolution fusion is proposed. Specially, the network architecture is composed of two novel modules, the Temporal-Channels Attention Module (TCA) and Multiscale Temporal Convolution Fusion module (MTCF). TCA module is designed to generate a temporal-channel attention matrix for different visual channels and temporal features, motivating the CNN to focus on the critical category-associated feature representation learning. Along the channels, MTCF module adapts the grouped residual connections to flexibly extend the convolutional temporal receptive field, without introducing additional parameters. By reverse stacking, MTCF module creates a bidirectional information interaction among inter-channels, compensating for the receptive field and information imbalance between subgroups from different branches. The proposed method was evaluated on three benchmark datasets, including NTU RGB-D, NTU RGB-D120 and FineGYM. The results show that the proposed TCA-MTCF method improves the CNNs’ ability to model long-term temporal features of skeleton sequences, achieving the state-of-the-art performance for HAR.

Skeleton-based human action recognition aims to recognize human actions from given skeleton sequences. The literature utilizes fixed-stride sampling and uniform aggregations, which are independent of the input data and do not focus on representative motion frames. In this paper, to overcome the challenge of the fixed uniform aggregation strategy being unable to focus on discriminative motion information, a novel non-uniform motion aggregation embedded with a graph convolutional network (NMA-GCN) is proposed for skeleton-based human action recognition. Based on the skeleton quality and motion-salient regions, NMA is able to focus on the discriminative motion information of human motion-salient regions. Finally, the aggregated skeleton sequences are embedded with the GCN backbone for skeleton-based human action recognition. Experiments were conducted on three large benchmarks: NTU RGB+D, NTU RGB+D 120, and FineGym. The results show that our method achieves 93.4% (Xsub) and 98.2% (Xview) on NTU RGB+D dataset, 87.0% (Xsub) and 90.0% (Xset) on the NTU RGB+D 120 dataset, and 90.3% on FineGym dataset. Ablation studies and evaluations across various GCN-based backbones further support the effectiveness and generalization of NMA-GCN.

Distributed photovoltaic (DPV) power sites in industrial parks are characterized by dispersed layouts, practical fault detection environments, and high safety requirements. Conventional manual DPV O&M systems using handheld sensors are inefficient, expensive, and struggle with fault detection due to sparse industrial data and uni-modal information limitations. To this, this paper proposes an innovative advanced algorithm for DPV fault detection in industrial parks, utilizing a new sparse industrial dataset, "SolarPark," collected via multi-modal UAVs and annotated through a multi-expert process with uncertainty scoring. By fusing the Convolutional Block Attention Module (CBAM), Bidirectional Feature Pyramid Network (BiFPN), Ghost modules, the algorithm enhances attention to critical photovoltaic fault-related channel information, strengthens multi-scale photovoltaic fault feature fusion, and achieves lightweight efficiency. Combined with multi-modal UAV videos, the proposed industrial DPV fault detection algorithm achieves a precision of 95.4%, effectively ensuring the efficiency of DPV power sites in data-scarce industrial scenarios. Extensive experiments on the developed cloud platform confirm the proposed algorithm’s efficient, cost-effective, and easy to deploy for aerial inspections of DPV O&M systems.