# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 1 Introduction
However, accurate registration in the ocean area is challenging, so ocean-going ship target detection should refrain from registration processing [14]. In reference [15], based on single frame discrimination based on SOLOv2, an autocorrelation-filtering algorithm was used to form the trajectory association of the moving target and remove the false alarm twice to obtain the target motion information. This method is associated with the problems of a large amount of calculation, strenuous training, and strict requirements on the accuracy of auxiliary image information. In literature [16], constant false alarm detection combined with LeNet network identification was used, and the middle latitude method was used to associate trajectories with global nearest neighbors. Although the calculation amount was small, it was relatively simple to select test data clouds, which was challenging to cover complex scenarios in practical applications. In addition, literature [17] uses the concatenated anchor-assisted detection network (CR2A-NET) to preprocess RESNET34 to remove many false alarms in the air and sea area and the coarse and fine structure network to complete the ship target in any direction. This architecture has become a relatively common method in ship detection and has good adaptability to high-resolution airborne remote sensing. However, it is difficult for RESNET34 to distinguish the blurred object from the interference cloud for low-resolution images. In literature [18], the Multiple Hypothesis Tracking (MHT) algorithm was used to track the target and eliminate the false target to obtain the motion state. However, this method depended on the accuracy of the segmentation stage, and it could not stop the cloud fragmentation target with similar motion information. More methods are necessary, i.e., than the existing methods, to mine the hidden information in the sequence observation data, or the existing methods focus on the sequence association of the target and need more judgment of the characteristics of the target [19]. Low-resolution wide-area remote sensing target detection mainly removes some interference through target characteristics and visual attention mechanism [20]. In addition, it removes false alarms through association and motion state in big data. Aiming at the problems of the previous design methods, this paper proposes a real-time ship target detection method based on sequence images to apply space-ground integration real-time information acquisition. Firstly, the saliency enhancement calculation was carried out for the low-resolution dim target, and the contrast between the gray level of the target and the surrounding background was improved by iterative morphological reconstruction. Furthermore, the feature inconsistency caused by minor pixel interference, gray level noise, and edge dispersion was removed to enhance the integrity of the target extraction. Then, the contrast between the target and the surrounding noise is used to realize the adaptive segmentation calculation of the front and rear scenes, and the corresponding position of the suspected target at the pixel level is obtained. Then, according to the simplified HOG feature and support vector machine, most of the false alarm targets were eliminated to get the second-level suspected target results, and the target sequence was obtained according to the local prediction search of sequence images and the non-sequence false alarm targets (including stationary ship targets) were eliminated. Finally, the lightweight CNN network S-YOLO is used to identify the target sequence and finally identify the remaining targets. Because of the multi-level identification, the part with a significant computational load decreases with the gradual reduction of suspected targets, which can effectively meet the accuracy and guarantee the real-time performance of the calculation. This paper is arranged as follows. Section 2 presents the detection and recognition algorithm of low-resolution sequence targets, and the calculation steps are introduced in detail.