Coral segmentation poses unique challenges due to its irregular morphology and camouflage-like characteristics. These factors often result in low precision, large model parameters, and poor real-time performance. To address these issues, this paper proposes a novel coral instance segmentation (CIS) network model. Initially, we designed a novel downsampling module, ADown_HWD, which operates at multiple resolution levels to extract image features, thereby preserving crucial information about coral edges and textures. Subsequently, we integrated the bi-level routing attention (BRA) mechanism into the C2f module to form the C2f_BRA module within the neck network. This module effectively removes redundant information, enhancing the ability to distinguish coral features and reducing computational redundancy. Finally, dynamic upsampling, Dysample, was introduced into the CIS to better retain the rich semantic and key feature information of corals. Validation on our self-built dataset demonstrated that the CIS network model significantly outperforms the baseline YOLOv8n model, with improvements of 6.3% and 10.5% in P_B and P_M and 2.3% and 2.4% in mAP50_B and mAP50_M, respectively. Furthermore, the reduction in model parameters by 10.1% correlates with a notable 10.7% increase in frames per second (FPS) to 178.6, thus effectively meeting real-time operational requirements.

Foggy and hazy weather conditions can significantly reduce the clarity of images captured by cameras, making it difficult for object detection algorithms to accurately recognize targets. This degradation can cause failures in autonomous or assisted driving systems, posing severe safety threats to both drivers and passengers. To address the issue of decreased detection accuracy in foggy weather, we propose an object detection algorithm specifically designed for such environments, named AODs-CLYOLO. To effectively handle images affected by fog, we introduce an image dehazing model, AODs, which is more suitable for detection tasks. This model incorporates a Channel–Pixel (CP) attention mechanism and a new Contrastive Regularization (CR), enhancing the dehazing effect while preserving the integrity of image information. For the detection network component, we propose a learnable Cross-Stage Partial Connection Module (CSPCM++), which is used before the detection head. Alongside this, we integrate the LSKNet selective attention mechanism to improve the extraction of effective features from large objects. Additionally, we apply the FocalGIoU loss function to enhance the model’s performance in scenarios characterized by sample imbalance or a high proportion of difficult samples. Experimental results demonstrate that the AODs-CLYOLO detection algorithm achieves up to a 10.1% improvement in the mAP (0.5:0.95) metric compared to the baseline model YOLOv5s.

With the rapid development of autonomous driving and robotics, the demand for high-precision depth estimation is growing steadily. The emergence of self-supervised learning methods has reduced the reliance on expensive and hard-to-obtain ground truth data. However, existing models still face challenges in design, such as insufficient detail recovery, inadequate feature fusion, and limited global contextual understanding, which lead to deficiencies in the accuracy and precision of the generated depth maps. To address these challenges, this paper proposes a novel self-supervised monocular depth estimation network, Enhanced Depth Fusion Net (EDFNet). Based on the encoder-decoder architecture, EDFNet introduces three key modules: the Adaptive Selective Attention Module (ASAM), the Multi-Dimensional Attention Fusion Module (MDAFM), and the Hybrid Parallel Convolution Block (HPCB). Specifically, ASAM selectively emphasizes critical features and spatial locations in images to enhance the model’s ability to capture details. MDAFM captures feature information from different perspectives and levels to address the problem of critical information loss, while HPCB effectively captures and fuses local and global information through parallel processing. The synergy of these modules enables EDFNet to generate high-quality depth maps in various complex scenarios. Experimental results on the KITTI dataset demonstrate that the proposed method significantly outperforms state-of-the-art approaches across multiple evaluation metrics, including Abs Rel, Sq Rel, RMSE, δ <1.25, effectively mitigating the issues of detail loss and edge blurring in depth maps. Additionally, EDFNet demonstrates excellent generalization performance on the Make3D, NYU Depth V2, and Cityscapes datasets.

To improve the prediction accuracy of monocular depth estimation networks and address issues such as edge blurring and excessive artifacts in the generated depth maps, this paper proposes a deep network architecture based on a global-local feature fusion module and a residual pooling module. The encoder utilizes a Hierarchical Transformer, while the decoder incorporates a U-Net structure model that combines multi-dimensional attention features aggregation and residual pooling. The residual pooling module facilitates better extraction of background contextual information from the feature maps to obtain more precise scene depth information. The global-local feature fusion module enables the network to learn features that encompass both global and local information. Experimental evaluations conducted on the NYU Depth V2 and KITTI datasets demonstrate that the proposed method achieves a δ1 of 0.916 on the NYU Depth V2 dataset, along with enhanced generalization ability and robustness. Furthermore, the effectiveness of each module is validated through ablation studies on the NYU Depth V2 dataset.