Satellite remote sensing technology can achieve realtime observation of ships at sea, and the remote sensing images obtained have the advantages of high contrast and low noise and have become one of the important means of marine monitoring. For the satellite remote sensing image data, there are two main problems: (1) Remote sensing data class-imbalance problem; (2) the existing target detector in the presence of clouds, islands, farmed nets and other interferences on the small target ship there is a leakage of detection and wrong detection problem. To address the above problems, firstly, a new dataset containing 3881 images of remotely sensed ships in a variety of complex environments is constructed, which contains a total of 8418 ship instances. Secondly, we propose CSDP-YOLO for small target ship detection method with remote sensing data class imbalance. In order to enhance the performance of neural networks for small target ship detection in remote sensing images, the innovative CSDP module is proposed, which uses deep large kernel convolution to enhance the sensory field of shallow features and mixes the channel positions using point convolution to obtain a more excellent feature extraction performance. Finally, the MPDIoU loss function is introduced to solve the class imbalance problem between remote sensing small target ships and the background. We compare the performance with other state-of-the-art algorithms. The experimental results show that the proposed CSDP-YOLO algorithm can significantly improve the performance of small target ship detection for private datasets. Its average precision, recall, and AP50 are improved to 90.1%, 86.6%, and 91.4%, respectively. For the SSDD public remote sensing dataset, its metrics can reach the highest 93.6%, 93.7%, and 96.8%, respectively. For datasets and codes related to this article, please contact the corresponding author

Visual simultaneous localization and mapping (vSLAM) is a critical technology for enabling robots to achieve self-awareness in unknown environments. However, the robustness and precision of vSLAM are continuously challenged by issues, such as sudden changes in lighting, reflections from shadows, and reduced contrast within indoor settings under constrained hardware resources. This article introduces an improved ORB-SLAM3 algorithm based on image enhancement for constrained hardware resources adaptive-light enhanced visual simultaneous localization and mapping (ALE-vSLAM), aimed at addressing challenges in varying lighting conditions. First, we have released a proprietary data set specifically targeting issues related to lighting variations, combined with image enhancement techniques to categorize and process the textural and structural features of images under different lighting conditions. Second, to more accurately extract feature points across various scales, we propose an adaptive contrast-guided contrast limited adaptive histogram equalization (CLAHE) enhancement method, in conjunction with an image pyramid. Finally, we have developed a CLAHE-integrated features from the accelerated segment test corner detection strategy that reincorporates important feature points filtered out due to lighting changes, while mitigating noise impact as much as possible. The experiments utilized the European robotics challenge data set and our own data set (SLAMLightClass data set). Results indicate that under conditions of limited resources, ALE-vSLAM consistently outperforms the ORB-SLAM3 algorithm in both positioning accuracy and the robustness of the feature extraction.

In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.

Multimedia data has the characteristics of large scale and skewed distribution with a long-tailed shape, which is a challenging imbalance problem faced by deep learning. In long-tailed image instance segmentation, the existing methods deal with this imbalance problem from a single perspective, ignoring the presence of multiple imbalance factors, which results in the limitation of performance. Considering that imbalances exist not only between positive and negative classes, but also between foreground and background subclasses, as well as between hard and easy examples, we argue that the losses of samples should be hierarchically equalized at multi-levels (HEL). In line with this idea, we first propose a focus based hierarchical-equalization loss (FHEL), which employs a class gradient ratio based reweighting mechanism to achieve the balance between classes, and uses a subclass-balance term and a sample-balance term to separately deal with the inter-subclass and inter-sample imbalances. FHEL can improve the performance of long-tailed instance segmentation in an end-to-end manner, avoiding the overfitting risk and manual hard division in the traditional methods. On the basis of FHEL, we further explore the relationship between inter-subclass imbalance and inter-sample imbalance, and propose a constrained-focus based hierarchical-equalization loss (CFHEL) that copes with the imbalances at multi-levels simultaneously with fewer hyperparameters. CFHEL is effective and easy to tune hyperparameters. We conduct extensive experiments on LVIS v1.0 and COCO-LT datasets with different benchmarks. Both FHEL and CFHEL are superior to the existing methods. On LVIS v1.0, with ResNet50 Mask R-CNN, ResNet101Mask R-CNN, ResNeXt101 Mask R-CNN and ResNet101 Cascade Mask R-CNN, CFHEL outperforms its baselines respectively with 19.8%, 18.5%, 21.6% and 21.2% AP% gains, and with 6.7%, 6.6% and 6.5% AP gains, achieving the new state-of-the-arts. On COCO-LT, our CFHEL outperforms the baseline with 13.2% tail AP gains and 3.3% whole AP gains, also achieving the new best performances.