Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385625363> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4385625363 abstract "The research led to a change in direction or approach toward automated object detection on images and video. Some challenges in the detection of video make them complex. The mobility of objects and the complexity of low-light conditions make object detection a highly important field of research. Many researchers have proposed several approaches for detecting the object in images and video. This paper approaches the deep-learning-based YOLO (You Only Look Once) algorithms for object detection on image and video sequences. We proposed YOLOv7 and YOLOv8 algorithms with an apple dataset for this experiment which has achieved Mean Average Precision is 94.2% and 91.2% respectively. Where the recall and precision are 98%, 93.8% for YOLOV7, and 98%, 93.9% for YOLOv8. In such cases, fine-tune the model's hyperparameters or train models on a more balanced dataset to improve its performance. All the positive predictions were made by the classifiers and identified the actual positive instances in the dataset. The algorithms process 120 frames per second with GPU, while YOLOv8 performed well and had more accurate results on images and video within 1.3ms speed per image size at 640x640. In addition to comparing the experimental models, this work focuses on object detection on images and videos taken in various complicated situations." @default.
- W4385625363 created "2023-08-08" @default.
- W4385625363 creator A5011802507 @default.
- W4385625363 creator A5014253334 @default.
- W4385625363 creator A5027504677 @default.
- W4385625363 creator A5037426155 @default.
- W4385625363 creator A5059073882 @default.
- W4385625363 creator A5060107034 @default.
- W4385625363 date "2023-06-23" @default.
- W4385625363 modified "2023-09-23" @default.
- W4385625363 title "Deep Learning-Based Object Detection Algorithms on Image and Video" @default.
- W4385625363 cites W2183182206 @default.
- W4385625363 cites W2603203130 @default.
- W4385625363 cites W2963037989 @default.
- W4385625363 cites W2963150697 @default.
- W4385625363 cites W2989604896 @default.
- W4385625363 cites W3035905805 @default.
- W4385625363 cites W3214206881 @default.
- W4385625363 cites W4312187461 @default.
- W4385625363 doi "https://doi.org/10.1109/conit59222.2023.10205601" @default.
- W4385625363 hasPublicationYear "2023" @default.
- W4385625363 type Work @default.
- W4385625363 citedByCount "0" @default.
- W4385625363 crossrefType "proceedings-article" @default.
- W4385625363 hasAuthorship W4385625363A5011802507 @default.
- W4385625363 hasAuthorship W4385625363A5014253334 @default.
- W4385625363 hasAuthorship W4385625363A5027504677 @default.
- W4385625363 hasAuthorship W4385625363A5037426155 @default.
- W4385625363 hasAuthorship W4385625363A5059073882 @default.
- W4385625363 hasAuthorship W4385625363A5060107034 @default.
- W4385625363 hasConcept C108583219 @default.
- W4385625363 hasConcept C111919701 @default.
- W4385625363 hasConcept C11413529 @default.
- W4385625363 hasConcept C115961682 @default.
- W4385625363 hasConcept C153180895 @default.
- W4385625363 hasConcept C154945302 @default.
- W4385625363 hasConcept C182521987 @default.
- W4385625363 hasConcept C202444582 @default.
- W4385625363 hasConcept C2776151529 @default.
- W4385625363 hasConcept C2781238097 @default.
- W4385625363 hasConcept C31510193 @default.
- W4385625363 hasConcept C31972630 @default.
- W4385625363 hasConcept C33923547 @default.
- W4385625363 hasConcept C41008148 @default.
- W4385625363 hasConcept C4641261 @default.
- W4385625363 hasConcept C71681937 @default.
- W4385625363 hasConcept C81669768 @default.
- W4385625363 hasConcept C8642999 @default.
- W4385625363 hasConcept C9652623 @default.
- W4385625363 hasConcept C98045186 @default.
- W4385625363 hasConceptScore W4385625363C108583219 @default.
- W4385625363 hasConceptScore W4385625363C111919701 @default.
- W4385625363 hasConceptScore W4385625363C11413529 @default.
- W4385625363 hasConceptScore W4385625363C115961682 @default.
- W4385625363 hasConceptScore W4385625363C153180895 @default.
- W4385625363 hasConceptScore W4385625363C154945302 @default.
- W4385625363 hasConceptScore W4385625363C182521987 @default.
- W4385625363 hasConceptScore W4385625363C202444582 @default.
- W4385625363 hasConceptScore W4385625363C2776151529 @default.
- W4385625363 hasConceptScore W4385625363C2781238097 @default.
- W4385625363 hasConceptScore W4385625363C31510193 @default.
- W4385625363 hasConceptScore W4385625363C31972630 @default.
- W4385625363 hasConceptScore W4385625363C33923547 @default.
- W4385625363 hasConceptScore W4385625363C41008148 @default.
- W4385625363 hasConceptScore W4385625363C4641261 @default.
- W4385625363 hasConceptScore W4385625363C71681937 @default.
- W4385625363 hasConceptScore W4385625363C81669768 @default.
- W4385625363 hasConceptScore W4385625363C8642999 @default.
- W4385625363 hasConceptScore W4385625363C9652623 @default.
- W4385625363 hasConceptScore W4385625363C98045186 @default.
- W4385625363 hasLocation W43856253631 @default.
- W4385625363 hasOpenAccess W4385625363 @default.
- W4385625363 hasPrimaryLocation W43856253631 @default.
- W4385625363 hasRelatedWork W2021207813 @default.
- W4385625363 hasRelatedWork W2095705906 @default.
- W4385625363 hasRelatedWork W2129974284 @default.
- W4385625363 hasRelatedWork W2223320490 @default.
- W4385625363 hasRelatedWork W2548411843 @default.
- W4385625363 hasRelatedWork W2782964878 @default.
- W4385625363 hasRelatedWork W2922421953 @default.
- W4385625363 hasRelatedWork W2975200075 @default.
- W4385625363 hasRelatedWork W3002270006 @default.
- W4385625363 hasRelatedWork W4239236498 @default.
- W4385625363 isParatext "false" @default.
- W4385625363 isRetracted "false" @default.
- W4385625363 workType "article" @default.