• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.31-40
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• 2022

YOLOv4 can be used for detecting parking vehicles in order to check a vehicle in out-door parking space. YOLOv4 has 9 anchor boxes in each of 13x13 grid cells for detecting a bounding box of object. Because anchor boxes are allocated based on each cell, there can be existed small observational error for detecting real objects due to the distance between neighboring cells. In this paper, we proposed YOLOv4 grid cell shift algorithm for improving the out-door parking vehicle detection accuracy. In order to get more chance for trying to object detection by reducing the errors between anchor boxes and real objects, grid cells over image can be shifted to vertical, horizontal or diagonal directions after YOLOv4 basic detection process. The experimental results show that a combined algorithm of a custom trained YOLOv4 and a cell shift algorithm has 96.6% detection accuracy compare to 94.6% of a custom trained YOLOv4 only for out door parking vehicle images.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.318-324
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• 2017

We introduced a stereo camera on the aircraft to detect flight objects and to estimate the 3D position of them. The Saliency map algorithm based on PCT was proposed to detect a small object between clouds, and then we processed a stereo matching algorithm to find out the disparity between the left and right camera. In order to extract accurate disparity, cost aggregation region was used as a variable region to adapt to detection object. In this paper, we use the detection result as the cost aggregation region. In order to extract more precise disparity, sub-pixel interpolation is used to extract float type-disparity at sub-pixel level. We also proposed a method to estimate the spatial position of an object by using camera parameters. It is expected that it can be applied to image - based object detection and collision avoidance system of autonomous aircraft in the future.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.307-313
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• 2021

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.909-919
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• 2023

Benthic marine invertebrates, the invertebrates living on the bottom of the ocean, are an essential component of the marine ecosystem, but excessive reproduction of invertebrate grazers or pirate creatures can cause damage to the coastal fishery ecosystem. In this study, we compared and evaluated You Only Look Once Version 7 (YOLOv7), the most widely used deep learning model for real-time object detection, and detection tansformer (DETR), a transformer-based model, using underwater images for benthic marine invertebratesin the coasts of South Korea. YOLOv7 showed a mean average precision at 0.5 (mAP@0.5) of 0.899, and DETR showed an mAP@0.5 of 0.862, which implies that YOLOv7 is more appropriate for object detection of various sizes. This is because YOLOv7 generates the bounding boxes at multiple scales that can help detect small objects. Both models had a processing speed of more than 30 frames persecond (FPS),so it is expected that real-time object detection from the images provided by divers and underwater drones will be possible. The proposed method can be used to prevent and restore damage to coastal fisheries ecosystems, such as rescuing invertebrate grazers and creating sea forests to prevent ocean desertification.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.21-26
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• 2024

The one of the famous deep learning models for object detection task is you only look once version 5 (YOLOv5) framework based on the one stage architecture. In addition, YOLOv5 model indicated high performance for accurate lesion detection using the bottleneck CSP layer and skip connection function. The purpose of this study was to evaluate the performance of YOLOv5 framework according to various hyperparameters in position emission tomogrpahy (PET) phantom images. The dataset was obtained from QIN PET segmentation challenge in 500 slices. We set the bounding box to generate ground truth dataset using labelImg software. The hyperparameters for network train were applied by changing optimization function (SDG, Adam, and AdamW), activation function (SiLU, LeakyRelu, Mish, and Hardwish), and YOLOv5 model size (nano, small, large, and xlarge). The intersection over union (IOU) method was used for performance evaluation. As a results, the condition of outstanding performance is to apply AdamW, Hardwish, and nano size for optimization function, activation function and model version, respectively. In conclusion, we confirmed the usefulness of YOLOv5 network for object detection performance in nuclear medicine images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.174-177
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• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.