Ship detection in remote sensing plays an important role in civil and military fields. Owing to the complex background and uncertain direction, ship detection is full of challenge by using the commonly used object-detection methods. In this article, a new framework for detecting the arbitrary direction ships is proposed based on the improvement in the Faster region-based convolutional network (R-CNN), in which the shape of the bounding box is described by three sides, namely, vertical side, horizontal side, and short side, respectively. The inclination of the ship is obtained by calculating the arc-tangent value of the vertical side to the horizontal side. First, the better performing ResNet-101 is adopted to extract features over an entire image, which are shared by the region proposal network (RPN) and the head network. Then, the multidirection proposal regions that may contain ships are generated by the RPN. Next, the global and local features of the proposal regions are combined as the whole features of the regions by a multiregion feature-fusion (MFF) module, which can provide more detailed information of the regions. Finally, the head network uses the whole features of the proposal regions for bounding-box recognition through multitask learning, including classification, regression, and incline direction prediction (left or right). The proposed method is tested and compared with other state-of-the-art ship-detection methods on two open remote-sensing data sets and some large-scale and real images. The experimental results validate that the proposed approach has achieved better performance.

The traditional construction industry is characterized by high energy consumption and significant carbon emissions, primarily due to its reliance on on-site manual labor and wet operations, which are not only low in mechanization but also result in low material efficiency and substantial construction waste. Prefabricated construction offers a new solution with its efficient production methods, significantly enhancing material utilization and construction efficiency. This paper focuses on the production scheduling optimization of prefabricated components. The production scheduling directly affects the construction speed and cost of prefabricated buildings. Given the complex modeling and numerous constraints faced by the production of prefabricated components, we propose an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. The algorithm incorporates adaptive operators and greedy concepts for local search, enhancing solution exploration and diversity. We segment the production of prefabricated components into six stages, analyzing dependencies and constraints, and form a comprehensive scheduling model with objectives of minimizing contract penalties, storage costs, and production time. Extensive experiments demonstrate that the improved NSGA-II provides a more balanced and larger set of solutions compared to baseline algorithms, offering manufacturers a wider range of options. This research contributes to the optimization of production scheduling in the prefabricated construction industry, supporting coordinated, sustainable, automated, and transparent production environments.

In the era of big data, how to encrypt image and ensure image security is an important research hot spot. In this paper, an efficient image encryption scheme is put forward based on Slant Haar Type Orthogonal transform (SHTOT) and compressed sensing (CS) combined with chaotic system. First, the original image is transformed by our proposed SHTOT, which contains specific parameters that may be regarded as encryption key. Then, the transformed coefficients are compressed and measured simultaneously by using CS, during which some pseudo random sequences produced by a chaotic system coupling sine mapping and logistic mapping are employed to generate the measurement matrix for CS. Next, Arnold transform is utilized to the scrambling of the CS measured values. Based on this, some other pseudo random sequences are used to the modification of the quantized CS measured values. Finally, the decryption operation is performed according to the reverse process described above and a blind Sparsity Adaptive Matching Pursuit algorithm in CS is applied to the image reconstruction. Simulation and experimental analysis demonstrate that the image encryption scheme provided in this paper has good performance in the image compression and encryption from the perspective of visual effect, information entropy, correlation coefficient, key sensitivity, key space and robustness.