Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. Existing methods often use image enhancement to improve detection, which results in a large amount of computational resource consumption. To address these challenges, this paper proposes a detection method, 3L-YOLO, based on YOLOv8n, which eliminates the need for image enhancement modules. First, we introduce switchable atrous convolution (SAConv) into the C2f module of YOLOv8n, improving the model’s ability to efficiently capture global contextual information. Second, we present a multi-scale neck module that aggregates shallow features and incorporates a channel attention mechanism to prioritize the most relevant features. Third, we introduce a dynamic detection head, which employs a cascade of spatial, scale, and channel attention mechanisms to enhance detection accuracy and robustness. Finally, we replace the original loss function with MPDIoU loss, improving bounding box regression and overall reliability. Additionally, we create a synthetic low-light dataset to evaluate the performance of the proposed method. Extensive experiments on the ExDark, ExDark+, and DARK FACE datasets demonstrate that 3L-YOLO outperforms YOLOv8n in low-light object detection, with improvements in mAP@0.5 of 2.7%, 4.3%, and 1.4%, respectively, across the three datasets. In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs.

The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities.