Achieving accurate object detection has always been a highly challenging task in the field of computer science, particularly in remote sensing image object detection. The possibility of objects appearing in any direction adds to the difficulty of achieving precise object detection. Additionally, the presence of small objects and the influence of environmental factors pose significant challenges for current deep learning-based object detection algorithms. In order to address these challenges, this paper proposes a target detection algorithm called YOLO-SH, which is based on YOLOv8. It improves the detection performance of objects appearing in any direction by incorporating the Swin Transformer structure into YOLO. The Swin Transformer utilizes sliding windows and hierarchical structures to achieve more efficient and flexible computation, and it can capture global information from multi-scale images with better feature extraction capabilities. Furthermore, to improve the detection performance for small objects, an additional prediction head is added, which effectively detects multi-scale objects based on the three original prediction heads in YOLOv8. Through extensive experiments on the DIOR dataset, our YOLO-SH model demonstrates excellent performance in remote sensing image detection.

Recently, drone scenes have been widely used in rescue, agriculture and other industries, and drones can fly at different altitudes to obtain multiple fields of view and viewing angles. However, since most of the drones are shot at higher altitudes, there are many small targets and objects in the shooting images. In view of these difficulties, this paper proposes a new network model :IT-YOLOX, which is based on YOLOX-tiny. First, an additional detection layer is added to the original three detection layers of YOLOX-tiny to detect small target objects. Secondly, iAFF module is used in YOLOX-tiny neck to fuse the feature information of backbone network. Finally, transformer structure is used in the last part of the backbone network. This paper conducted experiments on VisDrone2019, and the experimental results proved that the improved network improved by 5.17% compared with the baseline model (YOLOX-tiny) mAP, especially strengthening the recognition ability of small targets.

As one of the important components of the metro project, the pipeline synthesis is characterized by a wide variety of specialties and intricate arrangement, and multiple units and specialties are involved in the design and construction of each in the design process, which leads to the situation that various types of information data are difficult to interact with each other. To address this problem, the pipeline installation of a metro station in Qingdao is taken as the research object, and BIM technology is applied to the design and deepening of pipeline synthesis in metro engineering to establish the algorithm of automatic pipeline collision avoidance optimization. We combine the HiBIM plug-in to carry on the collision detection to the established full-professional pipeline comprehensive BIM model of subway station to solve the problems such as errors, omissions and deficiencies in the comprehensive design of subway station pipelines. The results show that the developed pipeline avoidance algorithm solves a total of 442 collision problems and saves a lot of time for pipeline avoidance optimization; after the specific deepening design and application of the integrated model of all professional pipelines in metro stations, it brings significant results in improving the design efficiency and quality, and provides positive thinking for the integrated design and deepening of pipelines in metro stations.

With Chinas increasing international status, there has been a rise in the number of Bangladeshi students studying in China. Consequently, more students from Bangladesh are learning Chinese pronunciation to aid them in their work, studies, and daily life. Without proper instruction, they may encounter difficulties in learning Chinese pronunciation and make more errors. To enhance the Chinese language proficiency of Bangladeshi international students, this paper proposes a method for analysing the morphology of Chinese consonants based on their morphological features. The method analyses Chinese consonants from the perspective of their pronunciation, and the extracted consonant data are used to draw consonant coordinate maps and vowel space maps. Comparative analyses were conducted to identify differences in Chinese vowel pronunciation between Chinese and Bangladeshi students. The analysis provides a theoretical guide to correct pronunciation for Bangladeshi students.