Research on Unmanned Vessel Surface Object Detection Based on Fusion of SSD and Faster-RCNN

2019 ◽  
Author(s):  
Xueqiang Song ◽  
Peng Jiang ◽  
He Zhu
Keyword(s):  
Object Detection ◽  
Surface Object

scholarly journals Water surface object detection using panoramic vision based on improved single-shot multibox detector

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Aofeng Li ◽  
Xufang Zhu ◽  
Shuo He ◽  
Jiawei Xia
Keyword(s):  
Object Detection ◽  
Water Surface ◽  
Detection Algorithm ◽  
Single Shot ◽  
Panoramic Vision ◽  
Feature Pyramid ◽  
The Mean ◽  
Detection Effect ◽  
Surface Object ◽  
Improved Algorithm

AbstractIn view of the deficiencies in traditional visual water surface object detection, such as the existence of non-detection zones, failure to acquire global information, and deficiencies in a single-shot multibox detector (SSD) object detection algorithm such as remote detection and low detection precision of small objects, this study proposes a water surface object detection algorithm from panoramic vision based on an improved SSD. We reconstruct the backbone network for the SSD algorithm, replace VVG16 with a ResNet-50 network, and add five layers of feature extraction. More abundant semantic information of the shallow feature graph is obtained through a feature pyramid network structure with deconvolution. An experiment is conducted by building a water surface object dataset. Results showed the mean Average Precision (mAP) of the improved algorithm are increased by 4.03%, compared with the existing SSD detecting Algorithm. Improved algorithm can effectively improve the overall detection precision of water surface objects and enhance the detection effect of remote objects.

scholarly journals Real-Time Water Surface Object Detection Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3523 ◽  
Author(s):  
Lili Zhang ◽  
Yi Zhang ◽  
Zhen Zhang ◽  
Jie Shen ◽  
Huibin Wang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Water Surface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
The North ◽  
Aspect Ratios ◽  
High Level ◽  
Surface Object

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.

scholarly journals Sea Surface Object Detection Algorithm Based on YOLO v4 Fused with Reverse Depthwise Separable Convolution (RDSC) for USV

2021 ◽  
Vol 9 (7) ◽  
pp. 753
Author(s):  
Tao Liu ◽  
Bo Pang ◽  
Lei Zhang ◽  
Wei Yang ◽  
Xiaoqiang Sun
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Network Size ◽  
Sea Surface ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Original Number ◽  
Speed And Accuracy ◽  
Surface Object

Unmanned surface vehicles (USVs) have been extensively used in various dangerous maritime tasks. Vision-based sea surface object detection algorithms can improve the environment perception abilities of USVs. In recent years, the object detection algorithms based on neural networks have greatly enhanced the accuracy and speed of object detection. However, the balance between speed and accuracy is a difficulty in the application of object detection algorithms for USVs. Most of the existing object detection algorithms have limited performance when they are applied in the object detection technology for USVs. Therefore, a sea surface object detection algorithm based on You Only Look Once v4 (YOLO v4) was proposed. Reverse Depthwise Separable Convolution (RDSC) was developed and applied to the backbone network and feature fusion network of YOLO v4. The number of weights of the improved YOLO v4 is reduced by more than 40% compared with the original number. A large number of ablation experiments were conducted on the improved YOLO v4 in the sea ship dataset SeaShips and a buoy dataset SeaBuoys. The experimental results showed that the detection speed of the improved YOLO v4 increased by more than 20%, and mAP increased by 1.78% and 0.95%, respectively, in the two datasets. The improved YOLO v4 effectively improved the speed and accuracy in the sea surface object detection task. The improved YOLO v4 algorithm fused with RDSC has a smaller network size and better real-time performance. It can be easily applied in the hardware platforms with weak computing power and has shown great application potential in the sea surface object detection.

Water Surface Object Detection Based on Deep Learning

2020 ◽  
Vol 57 (18) ◽  
pp. 181502
Author(s):  
刘雨青 Liu Yuqing ◽  
冯俊凯 Feng Junkai ◽  
邢博闻 Xing Bowen ◽  
曹守启 Cao Shouqi
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Water Surface ◽  
Surface Object

Fine-tuned Surface Object Detection Applying Pre-trained Mask R-CNN Models

2020 ◽  
Author(s):  
Haruhiro Fujita ◽  
Masatoshi Itagaki ◽  
Kenta Ichikawa ◽  
Yew Kwang Hooi ◽  
Kazuyoshi Kawahara ◽  
...  
Keyword(s):  
Object Detection ◽  
Surface Object

scholarly journals An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhiguo Zhou ◽  
Jiaen Sun ◽  
Jiabao Yu ◽  
Kaiyuan Liu ◽  
Junwei Duan ◽  
...  
Keyword(s):  
Object Detection ◽  
Water Surface ◽  
Large Scale ◽  
Autonomous Driving ◽  
Benchmark Dataset ◽  
Detection Methods ◽  
Climate Conditions ◽  
Detection Algorithms ◽  
Art Object ◽  
Surface Object

Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new object detector, named CRB-Net, is proposed to serve as a baseline. In experiments, CRB-Net was compared with 16 state-of-the-art object detection methods and outperformed all of them in terms of detection precision. In this paper, we further discuss the effect of the dataset diversity (e.g., instance size, lighting conditions), training set size, and dataset details (e.g., method of categorization). Cross-dataset validation shows that WSODD significantly outperforms other relevant datasets and that the adaptability of CRB-Net is excellent.

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