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5 results for “small object detection”
Small Object Aerial Person Detection Dataset
<p><strong>Small Object Aerial Person Detection Dataset:</strong></p> <p>The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.</p> <p> </p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>People</td> </tr> <tr> <td>Training</td> <td>2092</td> <td>40687</td> </tr> <tr> <td>Validation</td> <td>523</td> <td>10589</td> </tr> <tr> <td>Testing</td> <td>521</td> <td>10432</td> </tr> </tbody> </table> <p> </p> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Image Dataset for Object Detection of Small Size Construction Tools
<p> This is an image dataset established as input data for object detection model of small-sized construction tools. In the dataset, there are 12 classes of target tools (bucket, cutter, drill, grinder, hammer, knife, saw, shovel, spanner, tacker, trowel, and wrench) which are typically used at indoor construction sites. 25,084 sets of image and the corresponding label data have been established and shared. </p> <p> The diversity of objects in the images of the 12 small tools was considered by photographing tools of various shapes, sizes, and colors. In addition, to improve the model performance, images were also captured with various changes (e.g., image resolution, occlusion, lighting, and background). Among the 25,084 images in the dataset, 6,258 (25%) were obtained from the actual construction site. </p> <p> Object annotations in each image were done by bounding boxes and were saved into a text file. The coordinates of the bounding box have the form of (Class, Center X, Center Y, Width, Height). Class refers to one of 12 construction tool types. Center X and Center Y are the center coordinates of the bounding box for an object from an image when the resolution of the image has min-max normalized. Width and Height are the width and height of the bounding box for an object, respectively, also from the image with the min-max normalized resolution.</p> <p> </p> <p>The peer-reviewed publication for this dataset has now been published in " KSCE Journal of Civil Engineering" a Springer journal as follows:</p> <p><strong>* Lee, K., Jeon, C., and Shin, D. (2023, In press) "Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety" <em>KSCE Journal of Civil Engineering</em>. DOI: https://doi.org/10.1007/s12205-022-1011-7</strong></p> <p>Please cite this reference when using the dataset.</p>
artificial dataset of small engine parts for object detection-segmentation
<p>This dataset contains images and masks of parts used in engine assembly. The images were generated artificially from CAD models using gazebo simulator. The dataset consists of three classes: Large Bolt, Small Bolt and Rocker Arm. Annotations are represented as mask images with the same name as corresponding RGB images. 1080 images for each class, 3240 images total. Resolution of each image: 640x480 pixels. CAD models for each part are also included.</p>
DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method
<p>DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method</p>
Improvement of Lightweight Small Object Ship Detection Network Based on YOLOv7-tiny 模型文件
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