Water is the source of life, and oceans and rivers cover about 71% of the Earth’s area [1]. Rivers constitute a vital component of the global water cycle and serve a crucial function in facilitating the transfer [2]. However, recent global economic expansion and urbanization have caused significant damage to the natural environment [3] and severe water pollution [4]. Therefore, real-time monitoring of water environments has become crucial due to the ongoing deterioration of water quality. At present, the water’s surface is susceptible to problems, such as exposure or dim light due to the intensity of sunlight, which results in less information about the target features and difficult recognition results.

Object detection is one of the most widely used applications in UAV missions. Due to the angle and flight height of UAV photography, small objects account for a large proportion of UAV images compared to general scenes [5]. Small object detection has been one of the key difficulties in the field of object detection [6] with problems, such as invalid image feature information and blurred object features. To solve this problem, many scholars have proposed solutions. Xia et al. proposed an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation. In addition to collecting various sensor data, the platform can also use deep learning to detect small targets, such as vehicle trajectories [7]. Liu et al. proposed a model for tassel detection in maize, named yolov5-tassel. The authors trained the yolov5-tassel model based on UAV remote sensing images and achieved an mAP value of 44.7%, which is an improvement compared to FCOS, RetinaNet, and YOLOv5 in terms of detecting small tassels in maize [8]. Liu et al. proposed multibranch parallel feature pyramid networks (MPFPN) to detect small objects under UAV images. The MPFPN model applies the SSAM attention module [9] to attenuate the effect of background noise and uses cascade architecture in the Fast R-CNN stage to achieve a more powerful localization [10]. Chen et al. proposed small object detection networks based on a classification-oriented super-resolution generative adversarial network (CSRGAN), which is a model that adds classification branches and introduces classification losses into a typical SRGAN [11]. The experimental results demonstrate that CSRGAN outperforms VGG16 in classification [12]. In this study, UAV technology and deep learning are applied to small object detection at the same time. However, the water’s surface is susceptible to problems, such as exposure or dim light due to the intensity of sunlight, which results in less information about the water surface floater’s features and difficult recognition results. Deep learning models also face some problems when detecting a small water floater, such as small water surface floaters losing some of their features in the process of down-sampling, which leads to a lack of ability to extract features from global and low detection accuracy and poor feature recognition of a small water surface floater. Therefore, the direct application of the original YOLOv5 to UAV image object detection is not very effective. At the same time, UAV is also known as a powerful complement to the conventional water environment and assessment and has been gradually applied to water environment detection. So, this study proposes an efficient and accurate method based on YOLOV5 for the detection of small water surface floaters in UAV-captured images. The model can effectively locate the water surface floater and thus assist in the water surface floater salvage work.

2. Object Detection

3. 无人机捕获图像中的目标检测

4. 小型浮水车的物体检测