Multi-peg-hole assembly is a fundamental process in robotic manufacturing, particularly for circular aviation electrical connectors (CAECs) that require precise axial alignment. However, CAEC assembly poses significant challenges due to small apertures, posture disturbances, and the need for high error tolerance. This paper proposes a dual-stream Siamese network (DSSN) framework that fuses visual and tactile modalities to achieve accurate position identification in six-degree-of-freedom robotic connector assembly tasks. The DSSN employs ConvNeXt for visual feature extraction and SE-ResNet-50 with integrated attention mechanisms for tactile feature extraction, while a gated attention module adaptively fuses multimodal features. A bidirectional long short-term memory (Bi-LSTM) recurrent neural network is introduced to jointly model spatiotemporal deviations in position and orientation. Compared with state-of-the-art methods, the proposed DSSN achieves improvements of approximately 7.4%, 5.7%, and 5.4% in assembly success rates after 1, 5, and 10 buckling iterations, respectively. Experimental results validate that the integration of multimodal adaptive fusion and sequential spatiotemporal learning enables robust and precise robotic connectors assembly under high-tolerance conditions.

Low-light image enhancement (LLIE) methods based on Retinex theory often involve complex, multi-stage training and are commonly built on convolutional neural networks (CNNs). However, CNNs suffer from limitations in capturing long-range dependencies and often introduce redundant computations, leading to high computational costs. To address these issues, we propose a lightweight and efficient LLIE framework that incorporates an optimized CNN compression strategy and a novel attention mechanism. Specifically, we design a Spatial-Channel Feature Reconstruction Module (SCFRM) to suppress spatial and channel redundancy via split-reconstruction and separation-fusion strategies. SCFRM is composed of two parts, a Spatial Feature Enhancement Unit (SFEU) and a Channel Refinement Block (CRB), which together enhance feature representation while reducing computational load. Additionally, we introduce a Joint Attention (JOA) mechanism that captures long-range dependencies across spatial dimensions while preserving positional accuracy. Our Retinex-based framework separates the processing of illumination and reflectance components using a Denoising Network (DNNet) and a Light Enhancement Network (LINet). SCFRM is embedded into DNNet for improved denoising, while JOA is applied in LINet for precise brightness adjustment. Extensive experiments on multiple benchmark datasets demonstrate that our method achieves superior or comparable performance to state-of-the-art LLIE approaches, while significantly reducing computational complexity. On the LOL and VE-LOL datasets, our approach achieves the best or second-best scores in terms of PSNR and SSIM metrics, validating its effectiveness and efficiency.