Abstract
In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.
Highlights
In recent years, object detection technology has attracted a great deal of attention within the computer vision community [1,2,3,4,5], as an important component of many human-centric applications.Generally, object detection methods include background subtraction [6], frame difference [7], Hough transform [8], optical flow [9,10] and so on
Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster region-based convolutional neural network (R-CNN)
Our proposed method attempts to preprocess the negative effect of sunlight firstly via gamma correction [20], and we propose a deep learning network model based on Faster R-CNN, in which we integrate high-level features with low-level features in order to improve the detection accuracy
Summary
Object detection methods include background subtraction [6], frame difference [7], Hough transform [8], optical flow [9,10] and so on. These methods require hand-crafted models to extract specific features, and the extracted features have deficiencies in representativeness and robustness, which leads to poor generalization ability as a result. The problem of water environmental pollution is serious in some countries. In China, the government began to focus on this, and established the River Chief System, which requires the detection of water surface floats in a timely fashion. The administration usually monitors the river situation, relying on video surveillance or Sensors 2019, 19, 3523; doi:10.3390/s19163523 www.mdpi.com/journal/sensors
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