In single-frame detection methods, which have been widely applied because of the need for less prior knowledge compared with sequential detection methods [12], the single frame can be further divided by using background-characteristic-based [13; 14], object-characteristic-based [15; 16], and background-object-characteristic-combination-based [17; 18] methods. Background-characteristic-based methods are based on the estimation of the difference between the original image and the background. Object-characteristic-based methods are based on a saliency map constructed by using the local information of the objects [15; 16] and can be used to efficiently detect tiny objects based on infrared images. However, these methods are very sensitive to complex noise and show poor performance in tiny object detection in environments with complex and noisy maritime backgrounds, as high false alarm rates are caused by such background conditions. In tiny human object detection with strong noise, background noise suppression and object enhancement are crucial for methods based on visual saliency. The pixel intensity value in grayscale and infrared images can reflect the saliency of the object. However, strong noise generally has a similar pixel intensity value to that of tiny objects, which can cause a high false alarm rate. Tiny objects and noise in visible images may have similar saliency to those in grayscale images, but they usually have different color characteristics in the RGB channels, which can be used for tiny object enhancement in maritime environments with strong glint noise. In deep learning-based algorithms, performance is significantly worse when tiny objects are concealed by sun glint. Since noise can be introduced during image restoration, care should be taken when studying image denoising and reflection removal in images with tiny objects. In addition, strong glint noise can make a tiny object not noticeable when employing deep learning-based methods using feature learning [19; 20]. With the development of computer vision in recent years, UAVs' capability to automatically detect and recognize human attributes has become more relevant in the context of 24/24 h surveillance [21; 22; 23; 24; 25; 26; 27]. SeaDronesSee [27] is a large-scale dataset with high-resolution visible images captured by UAVs in which human targets are generally concealed by sun glint. This dataset has prompted research on tiny target detection for maritime SAR based on UAVs [3; 28; 29; 30; 31]. On this basis, we developed a method for sea surface sun glint suppression to achieve the enhancement and detection of tiny targets based on visual saliency. In summary, the existing methods for maritime human detection based on visual saliency present the following issues:  
1. When tiny human targets are concealed by sea surface sun glint, saliency-based methods have high false alarm rates, and deep learning-based methods have low recall. 2. When solely based on infrared and grayscale images, the saliency values of strong glint noise and targets are too similar to achieve noise suppression and object enhancement. 3. Object detection based on visual saliency is generally carried out on single-channel images, while visible images with tiny objects in maritime scenarios are seldom used for denoising and tiny object enhancement. Further, color characteristics are not studied effectively in maritime human target detection using saliency-based methods. To solve the above-mentioned problems, we propose a robust method to achieve maritime tiny human object detection in sun glint-containing images based on visual saliency(HDetect-VS), as shown in Figure 1. The contributions of this study are summarized as follows:  
1. Tiny object enhancement and detection under sea surface sun glint conditions is carried out based on visible images rather than single-channel images (infrared and grayscale images). The color characteristic differences between noise and objects are utilized to suppress strong glint noise and enhance object features. 2.