Optical Prior-Based Underwater Object Detection with Active Imaging

Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extracting object features. Unlike data-driven knowledge, the prior in our method is independent of training samples. The fundamental novelty of our approach lies in the integration of an image prior and the object detection task. This novelty is fundamental to the satisfying performance of our approach in underwater environments, which is demonstrated through comparisons with state-of-the-art object detection methods.

Download Full-text

Knowledge-Driven Network for Object Detection

Algorithms ◽

10.3390/a14070195 ◽

2021 ◽

Vol 14 (7) ◽

pp. 195

Author(s):

Yundong Wu ◽

Jiajia Liao ◽

Yujun Liu ◽

Kaiming Ding ◽

Shimin Li ◽

...

Keyword(s):

Object Detection ◽

Detection Methods ◽

Art Object ◽

Object Categories ◽

Complex Design ◽

Real World Applications ◽

Relationship Model ◽

Object Features ◽

The Relationship ◽

Object Relationship

Object detection is a challenging computer vision task with numerous real-world applications. In recent years, the concept of the object relationship model has become helpful for object detection and has been verified and realized in deep learning. Nonetheless, most approaches to modeling object relations are limited to using the anchor-based algorithms; they cannot be directly migrated to the anchor-free frameworks. The reason is that the anchor-free algorithms are used to eliminate the complex design of anchors and predict heatmaps to represent the locations of keypoints of different object categories, without considering the relationship between keypoints. Therefore, to better fuse the information between the heatmap channels, it is important to model the visual relationship between keypoints. In this paper, we present a knowledge-driven network (KDNet)—a new architecture that can aggregate and model keypoint relations to augment object features for detection. Specifically, it processes a set of keypoints simultaneously through interactions between their local and geometric features, thereby allowing the modeling of their relationship. Finally, the updated heatmaps were used to obtain the corners of the objects and determine their positions. The experimental results conducted on the RIDER dataset confirm the effectiveness of the proposed KDNet, which significantly outperformed other state-of-the-art object detection methods.

Download Full-text

An Improved Approach for Object Proposals Generation

Electronics ◽

10.3390/electronics10070794 ◽

2021 ◽

Vol 10 (7) ◽

pp. 794

Author(s):

Yao Deng ◽

Huawei Liang ◽

Zhiyan Yi

Keyword(s):

Object Detection ◽

Detection Methods ◽

True Positive ◽

Huge Number ◽

Object Boundary ◽

Detection Approach ◽

Object Proposals ◽

Joint Training ◽

Entire Object ◽

Candidate Object

The objectness measure is a significant and effective method used for generic object detection. However, several object detection methods can achieve accurate results by using more than 1000 candidate object proposals. In addition, the weight of each proposal is weak and also cannot distinguish object proposals. These weak proposals have brought difficulties to the subsequent analysis. To significantly reduce the number of proposals, this paper presents an improved generic object detection approach, which predicts candidate object proposals from more than 10,000 proposals. All candidate proposals can be divided, rather than preclassified, into three categories: entire object, partial object, and nonobject. These partial object proposals also display fragmentary information of the objectness feature, which can be used to reconstruct the object boundary. By using partial objectness to enhance the weight of the entire object proposals, we removed a huge number of useless proposals and reduced the space occupied by the true positive object proposals. We designed a neural network with lightweight computation to cluster the most possible object proposals with rerank and box regression. Through joint training, the lightweight network can share the features with other subsequent tasks. The proposed method was validated using experiments with the PASCAL VOC2007 dataset. The results showed that the proposed approach was significantly improved compared with the existing methods and can accurately detect 92.3% of the objects by using less than 200 proposals.

Download Full-text

Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

Applied Sciences ◽

10.3390/app10093280 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3280 ◽

Cited By ~ 3

Author(s):

Chinthakindi Balaram Murthy ◽

Mohammad Farukh Hashmi ◽

Neeraj Dhanraj Bokde ◽

Zong Woo Geem

Keyword(s):

Deep Learning ◽

Object Detection ◽

Pedestrian Detection ◽

Detection Methods ◽

Art Object ◽

Current State ◽

Learning Techniques ◽

Specific Object ◽

Benchmark Datasets ◽

Speed Up

In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.

Download Full-text

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

Sensors ◽

10.3390/s19163595 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3595 ◽

Cited By ~ 19

Author(s):

Anderson Aparecido dos Santos ◽

José Marcato Junior ◽

Márcio Santos Araújo ◽

David Robledo Di Martini ◽

Everton Castelão Tetila ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

Tree Species ◽

Remote Sensing Data ◽

Target Object ◽

Detection Methods ◽

Individual Tree ◽

Art Object

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Download Full-text

Real-Time Small Drones Detection Based on Pruned YOLOv4

Sensors ◽

10.3390/s21103374 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3374

Author(s):

Hansen Liu ◽

Kuangang Fan ◽

Qinghua Ouyang ◽

Na Li

Keyword(s):

Object Detection ◽

Real Time ◽

Processing Speed ◽

State Of The Art ◽

Detection Methods ◽

Detection Accuracy ◽

Small Object ◽

Art Object ◽

Real Time Detection ◽

Small Object Detection

To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.

Download Full-text

Scale-Aware Attention-Based PillarsNet (SAPN) Based 3D Object Detection for Point Cloud

Mathematical Problems in Engineering ◽

10.1155/2020/3927365 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xiang Song ◽

Weiqin Zhan ◽

Xiaoyu Che ◽

Huilin Jiang ◽

Biao Yang

Keyword(s):

Object Detection ◽

Autonomous Navigation ◽

Point Clouds ◽

Detection Performance ◽

Detection Methods ◽

Feature Maps ◽

3D Object ◽

3D Point Clouds ◽

Detection Approach ◽

3D Object Detection

Three-dimensional object detection can provide precise positions of objects, which can be beneficial to many robotics applications, such as self-driving cars, housekeeping robots, and autonomous navigation. In this work, we focus on accurate object detection in 3D point clouds and propose a new detection pipeline called scale-aware attention-based PillarsNet (SAPN). SAPN is a one-stage 3D object detection approach similar to PointPillar. However, SAPN achieves better performance than PointPillar by introducing the following strategies. First, we extract multiresolution pillar-level features from the point clouds to make the detection approach more scale-aware. Second, a spatial-attention mechanism is used to highlight the object activations in the feature maps, which can improve detection performance. Finally, SE-attention is employed to reweight the features fed into the detection head, which performs 3D object detection in a multitask learning manner. Experiments on the KITTI benchmark show that SAPN achieved similar or better performance compared with several state-of-the-art LiDAR-based 3D detection methods. The ablation study reveals the effectiveness of each proposed strategy. Furthermore, strategies used in this work can be embedded easily into other LiDAR-based 3D detection approaches, which improve their detection performance with slight modifications.

Download Full-text

Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images

Remote Sensing ◽

10.3390/rs13183776 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3776

Author(s):

Linlin Zhu ◽

Xun Geng ◽

Zheng Li ◽

Chun Liu

Keyword(s):

Object Detection ◽

Detection Method ◽

Feature Fusion ◽

Attention Mechanism ◽

Detection Methods ◽

Visual Object ◽

Art Object ◽

Geological Processes ◽

Landing Sites ◽

New Feature

It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

Download Full-text

A Two-Phase Mitosis Detection Approach Based on U-Shaped Network

BioMed Research International ◽

10.1155/2021/1722652 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Wenjing Lu

Keyword(s):

Deep Learning ◽

Object Detection ◽

State Of The Art ◽

Experimental Results ◽

Detection Methods ◽

Two Phase ◽

Mitosis Detection ◽

Detection Approach ◽

Histopathology Images ◽

Bounding Boxes

This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powerful object detection methods hardly be used in mitosis detection. Aiming at solving this problem, this paper firstly applies a CNN-based algorithm to pixelwisely segment the mitosis regions, based on which bounding boxes of mitosis are generated as strong labels. Based on the generated bounding boxes, an object detection network is trained to accomplish mitosis detection. Experimental results show that the proposed method is effective in detecting mitosis, and the accuracies outperform state-of-the-art literatures.

Download Full-text

Self-Adaptive Feature Transformation Networks for Object Detection in low luminance Images

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3480973 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-11

Author(s):

Shih-Chia Huang ◽

Quoc-Viet Hoang ◽

Da-Wei Jaw

Keyword(s):

Object Detection ◽

Extraction Process ◽

The Self ◽

Detection Methods ◽

Feature Transformation ◽

Detection Techniques ◽

Art Object ◽

Low Luminance ◽

Net Framework ◽

Self Adaptive

Despite the recent improvement of object detection techniques, many of them fail to detect objects in low-luminance images. The blurry and dimmed nature of low-luminance images results in the extraction of vague features and failure to detect objects. In addition, many existing object detection methods are based on models trained on both sufficient- and low-luminance images, which also negatively affect the feature extraction process and detection results. In this article, we propose a framework called Self-adaptive Feature Transformation Network (SFT-Net) to effectively detect objects in low-luminance conditions. The proposed SFT-Net consists of the following three modules: (1) feature transformation module, (2) self-adaptive module, and (3) object detection module. The purpose of the feature transformation module is to enhance the extracted feature through unsupervisely learning a feature domain projection procedure. The self-adaptive module is utilized as a probabilistic module producing appropriate features either from the transformed or the original features to further boost the performance and generalization ability of the proposed framework. Finally, the object detection module is designed to accurately detect objects in both low- and sufficient- luminance images by using the appropriate features produced by the self-adaptive module. The experimental results demonstrate that the proposed SFT-Net framework significantly outperforms the state-of-the-art object detection techniques, achieving an average precision (AP) of up to 6.35 and 11.89 higher on the sufficient- and low- luminance domain, respectively.

Download Full-text

Multi-View Object Detection Based on Deep Learning

Applied Sciences ◽

10.3390/app8091423 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1423 ◽

Cited By ~ 15

Author(s):

Cong Tang ◽

Yongshun Ling ◽

Xing Yang ◽

Wei Jin ◽

Chao Zheng

Keyword(s):

Deep Learning ◽

Object Detection ◽

Regression Models ◽

Detection Methods ◽

Detection Accuracy ◽

Single Shot ◽

Small Object ◽

Object Retrieval ◽

Detection Approach ◽

Small Object Detection

A multi-view object detection approach based on deep learning is proposed in this paper. Classical object detection methods based on regression models are introduced, and the reasons for their weak ability to detect small objects are analyzed. To improve the performance of these methods, a multi-view object detection approach is proposed, and the model structure and working principles of this approach are explained. Additionally, the object retrieval ability and object detection accuracy of both the multi-view methods and the corresponding classical methods are evaluated and compared based on a test on a small object dataset. The experimental results show that in terms of object retrieval capability, Multi-view YOLO (You Only Look Once: Unified, Real-Time Object Detection), Multi-view YOLOv2 (based on an updated version of YOLO), and Multi-view SSD (Single Shot Multibox Detector) achieve AF (average F-measure) scores that are higher than those of their classical counterparts by 0.177, 0.06, and 0.169, respectively. Moreover, in terms of the detection accuracy, when difficult objects are not included, the mAP (mean average precision) scores of the multi-view methods are higher than those of the classical methods by 14.3%, 7.4%, and 13.1%, respectively. Thus, the validity of the approach proposed in this paper has been verified. In addition, compared with state-of-the-art methods based on region proposals, multi-view detection methods are faster while achieving mAPs that are approximately the same in small object detection.

Download Full-text