It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a trade-off between the computational complexity and memory consumption. In this study, we evaluate the performance and complexity of different fusion operators in multi-modal object detection tasks. On top of that, a Poolformer-based fusion operator (PoolFuser) is proposed to enhance the accuracy of detecting targets without compromising the efficiency of the detection framework.

Fossil energy products such as liquefied natural gas (LNG) are among Canada's most important exports. Canadian engineers devote themselves to constructing visual surveillance systems for detecting potential LNG emissions in energy facilities. Beyond the previous infrared (IR) surveillance system, in this paper, a multimodal fusion-based LNG detection (MFLNGD) framework is proposed to enhance the detection quality by the integration of IR and visible (VI) cameras. Besides, a Fourier transformer is developed to fuse IR and VI features better. The experimental results suggest the effectiveness of the proposed framework.

Detection of the liquefied natural gas (LNG) leakage attracts increasing attention for preventing environments and governments from severe pollution and economic loss. Existing frameworks take advantage of stationary surveillance thermal cameras to detect the LNG leakage, which comprises background subtraction and leakage classification. However, these methods are limited in rural areas due to the lack of sensitivity and accuracy. In this article, a generalized framework, i.e., tensor-based leakage detection (TBLD), is proposed to detect LNG leakage in the rural area from surveillance thermal cameras. First, the proposed TBLD takes advantage of tensor factorization to fuse thermal image and corresponding gradient maps for improving sensitivity. Additionally, a finite-state-machine is designed to maintain leakage foreground along with the video streaming. The experiments demonstrate the robust performance of TBLD in the background subtraction stage. Second, multiple classification techniques are explored in the leakage classification stage. The results suggest that the TBLD can accurately detect the LNG leakage by applying 50 layers of residual networks (ResNet50). Finally, compared with contemporary frameworks, the TBLD has consistently improved performance concerning the different distances of LNG leakage. The experimental results demonstrate the effectiveness of the proposed TBLD, which also shows the great potential of TBLD in future industrial applications.

The integrity of geomagnetic data is a critical factor in understanding the evolutionary process of Earth's magnetic field, as it provides useful information for near-surface exploration, unexploded explosive ordnance detection, and so on. Aimed to reconstruct undersampled geomagnetic data, this paper presents a geomagnetic data reconstruction approach based on machine learning techniques. The traditional linear interpolation approaches are prone to time inefficiency and high labor cost, while the proposed approach has a significant improvement. In this paper, three classic machine learning models, support vector machine, random forests, and gradient boosting were built. Besides, a deep learning algorithm, recurrent neural network, was explored to further improve the training performance. The proposed learning models were used to specify a continuous regression hyperplane from a training data. The specified regression hyperplane is a mapping of the relation between the mock-up missing data and the surrounding intact data. Afterward, the trained models, essentially the hyperplanes, were used to reconstruct the missing geomagnetic traces for validation, and they can be used for reconstructing further collected new field data. Finally, numerical experiments were derived. The results showed that the performance of our methods was more competitive in comparison with the traditional linear method, as the reconstruction accuracy was increased by approximately 10%~20%.