Infrared and visible image fusion can integrate rich edge details and salient infrared targets, resulting in high-quality images suitable for advanced tasks. However, most available algorithms struggle to fully extract detailed features and overlook the interaction of complementary features across different modal images during the feature fusion process. To address this gap, this study presents a novel fusion method based on multi-scale edge enhancement and a joint attention mechanism (MEEAFusion). Initially, convolution kernels of varying scales were utilized to obtain shallow features with multiple receptive fields unique to the source image. Subsequently, a multi-scale gradient residual block (MGRB) was developed to capture the high-level semantic information and low-level edge texture information of the image, enhancing the representation of fine-grained features. Then, the complementary feature between infrared and visible images was defined, and a cross-transfer attention fusion block (CAFB) was devised with joint spatial attention and channel attention to refine the critical supplemental information. This allowed the network to obtain fused features that were rich in both common and complementary information, thus realizing feature interaction and pre-fusion. Lastly, the features were reconstructed to obtain the fused image. Extensive experiments on three benchmark datasets demonstrated that the MEEAFusion proposed in this research has considerable strengths in terms of rich texture details, significant infrared targets, and distinct edge contours, and it achieves superior fusion performance.

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input.

Active hydraulic mounts (AHMs) provide an effective solution for refining ride comfort noise and vibration in passenger cars. AHMs with inertia tracks, decoupler membranes, and oscillating coil actuators (AHM-IT-DM-OCAs) have been extensively studied owing to their compact structures and strong damping characteristics in the low-frequency band. This study focuses on the full parameter identification based on the distinct features of external dynamics, which can be used to obtain an accurate and reliable estimate of the transfer functions required for active control algorithms. A lumped parameter model was established for the AHM-IT-DM-OCAs, and the analytical frequency bands were defined by the two resonance frequencies of the fluid channel and actuator mover. Methods for nonlinear model simplification were proposed in different bands and verified theoretically. Based on the simplified models, the distinct features of the active and passive dynamics are successively revealed, which include three resonances and seven horizontal segments. Subsequently, a series of experimental studies on the distinct features were carried out, which agreed well with the theoretical results. However, owing to the limitations of the test equipment and fixture modalities, only the distinct features of one fixed point, two resonance peaks, and three horizontal segments can be used for parameter identification. Based on the validated distinct features, a procedure for full parameter identification is proposed, and all six key parameters are identified. The obtained results showed good consistency and rationality, indicating that this approach can be used for the transfer function estimation of the primary and secondary paths of the AHM-IT-DM-OCAs.