Due to low cost and ease of manufacture, the combined sensor consisted of a camera and a 1D laser range finder is commonly used in many engineering applications. Especially in aerospace field, this type of combined sensor is important to earth observation and deep space exploration. This article proposes a novel extrinsic calibration method to get the accurate and stable calibration results of camera-1D laser range finder combined sensor. Firstly, an accuracy criterion named with directional comprehensive criterion is proposed to solve the less constraint in traditional calibration methods, which can evaluate the inconsistencies in the 1D laser ranging direction and the mean reprojection error in the 2D image plane direction fully. Then, according to the proposed directional comprehensive criterion, a novel robust extrinsic calibration framework is presented, which removes the observation data with large errors automatically and utilizes a stability criterion as the termination condition. The simulation experiments and real data experiments reveal that the proposed method is effective, which is superior to the existing methods.

Dock is a significant site in the shipbuilding industry. The detection of docks contributes to many important fields. With the abundant methods and datasets, the deep learning-based object detection in remote sensing images has received wide attention. However, there is no dataset that includes the dock class. This paper firstly proposes a dock dataset to build a benchmark and advance dock detection research. Further, object detection of docks using existing methods cannot yield convincing results due to the characteristics of docks. To meet the challenges in dock detection, a novel deformable spatial attention module (DSAM) is proposed to enhance the feature representation and localization of docks. Based on the DSAM, a novel network architecture is proposed to perform accurate and efficient dock detection. The ablation and comparison experiments reveal that the proposed methods are accurate and effective, which are superior to the existing methods. Dock dataset can be downloaded in https://drive.google.com/drive/folders/1_ao1tOQtmO_aanoHXGPSkuqRRqWJaCjk?usp = share_link.

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.