ArticleSensors (Basel, Switzerland)2020
Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Enhancing UAV object detection with an efficient multi-scale feature fusion framework.PloS one · 2025Article
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- Instance-Level Contrastive Learning for Weakly Supervised Object Detection.Sensors (Basel, Switzerland) · 2022Article
- Content Swapping: A New Image Synthesis for Construction Sign Detection in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2022Article
- Teachers' Teaching Ability Promotion Strategies Based on Lightweight Deep Learning Combined with Target Detection Algorithm.Computational intelligence and neuroscience · 2022Article
- Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques.Medical devices (Auckland, N.Z.) · 2022Article
- A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching.Computational intelligence and neuroscience · 2021Article
- Design of a Scalable and Fast YOLO for Edge-Computing Devices.Sensors (Basel, Switzerland) · 2020Article
- A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles.Journal of imaging · 2020Review
- UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective.Sensors (Basel, Switzerland) · 2020Article
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7 authors.
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Abstract
Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices. Based on YOLO-LITE as the backbone network, Mixed YOLOv3-LITE supplements residual block (ResBlocks) and parallel high-to-low resolution subnetworks, fully utilizes shallow network characteristics while increasing network depth, and uses a "shallow and narrow" convolution layer to build a detector, thereby achieving an optimal balance between detection precision and speed when used with non-GPU based computers and portable terminal devices. The experimental results obtained in this study reveal that the size of the proposed Mixed YOLOv3-LITE network model is 20.5 MB, which is 91.70%, 38.07%, and 74.25% smaller than YOLOv3, tiny-YOLOv3, and SlimYOLOv3-spp3-50, respectively. The mean average precision (mAP) achieved using the PASCAL VOC 2007 dataset is 48.25%, which is 14.48% higher than that of YOLO-LITE. When the VisDrone 2018-Det dataset is used, the mAP achieved with the Mixed YOLOv3-LITE network model is 28.50%, which is 18.50% and 2.70% higher than tiny-YOLOv3 and SlimYOLOv3-spp3-50, respectively. The results prove that Mixed YOLOv3-LITE can achieve higher efficiency and better performance on mobile terminals and other devices.
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