ABSTRACT Object detection is the most crucial and challenging task of computer vision and has been used in various fields in recent years, such as autonomous driving and industrial inspection. Traditional object detection methods are mainly based on the sliding windows and the handcrafted features, which have problems such as insufficient understanding of image features and low accuracy of detection. With the rapid advancements in deep learning, convolutional neural networks (CNNs) and vision transformers have become fundamental components in object detection models. These components are capable of learning more advanced and deeper image properties, leading to a transformational breakthrough in the performance of object detection. In this review, we comprehensively review the representative object detection models from deep learning periods, tracing their architectural shifts and technological breakthroughs. Furthermore, we discuss key challenges and promising research directions in the object detection. This review aims to provide a comprehensive foundation for practitioners to enhance their understanding of object detection technologies.

Ships are usually disturbed by waves when they are traveling at sea. When the waves are large, it is not conducive to driving safety, comfort and economy. Therefore, this paper proposed a new type of automatic driving scheme, which links the wave height prediction with ship driving. By studying the accurate prediction of wave height, ships can adjust their course in real time to ensure that they always travel in the area with the lowest wave height. According to the different driving conditions of ships in the open sea and the offshore sea, we designed two wave height prediction models based on LSTM, which are suitable for the above two types of sea areas. In particular, when we created the open sea model, we selected the data of the location points other than the predicted point as the training data. After comparative testing, the two types of models have reached satisfactory accuracy, which provided support for the ship automatic driving scheme proposed in this paper.

Surface wave is the most energetic form of motions in the ocean and is crucially important to navigation safety and climate change. High-resolution global wave model plays a key role in accurate surface wave forecasting. However, operational forecasting systems are still not in high-resolution due to entailed high demand for large computation, as well as low parallel efficiency barrier. Here breakthroughs encompassing the design and application of irregular quasi-rectangular domain decomposition, master-slave cooperative computing workflow and pipelining scheme were applied to a global wave model, which has been used in several operational forecasting systems and earth system models. Our realistic surface wave simulations on Sunway TaihuLight Supercomputer demonstrated that our model had outstanding scalability and achieved 45.43 PFlops in ultra-high resolution of (1/100)°, using full-scale supercomputer with 10,649,600 cores. That provides a highly effective solution for accurate surface wave forecasting and climate change prediction.