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A Review of You Only Look Once Algorithms in Animal Phenotyping Applications. [PDF]
Animals (Basel)Li G, Jian R, Jun X, Shi G.
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Improved YOLOv2 Object Detection Model
2018 6th International Conference on Multimedia Computing and Systems (ICMCS), 2018Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2.
Rui Li, Jun Yang
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A pedestrian detection algorithm based on improved YOLOv2
2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), 2018For improving the accuracy of pedestrian detection, an improved algorithm based on YOLOv2 network framework is proposed. Usually, a large number of redundant candidate proposal boxes of detected pedestrians exist via using YOLOv2 framework. In this paper, clustering algorithm is adapted to obtain the priori knowledge of the pedestrian target proposal ...
Ziwei Liu
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A Lightweight YOLOv2 Object Detector Using a Dilated Convolution
2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2019In recent years, object detection approaches such as you-only-look-once (YOLO) have been getting a special attention owing to the emerging trend of autonomous driving systems. However, memory and computation complexity are usually known as the bottlenecks in implementing a YOLOv2 in hardware design. This study proposes a simple yet effective variant of
Hyuk-Jae Lee +2 more
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Highway Obstacle Detection Method Based on PP-YOLOv2
2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC), 2023Tianshu Liu
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Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2018
A frame object detection problem consists of two problems: one is a regression problem to spatially separated bounding boxes, the second is the associated classification of the objects within realtime frame rate. It is widely used in the embedded systems, such as robotics, autonomous driving, security, and drones - all of which require high-performance
Hiroki Nakahara +3 more
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A frame object detection problem consists of two problems: one is a regression problem to spatially separated bounding boxes, the second is the associated classification of the objects within realtime frame rate. It is widely used in the embedded systems, such as robotics, autonomous driving, security, and drones - all of which require high-performance
Hiroki Nakahara +3 more
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SSRN Electronic Journal, 2019
Monitoring traffic of India and calculating the peak hours and density count in a single day helps to develop a required travel and traffic volume estimates, which is required for satisfying all the needs in the planning of roads, its construction, its maintenance and overall administration of the state.
Snehal Kadam +3 more
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Monitoring traffic of India and calculating the peak hours and density count in a single day helps to develop a required travel and traffic volume estimates, which is required for satisfying all the needs in the planning of roads, its construction, its maintenance and overall administration of the state.
Snehal Kadam +3 more
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An OpenCL-Based FPGA Accelerator for Compressed YOLOv2
2019 International Conference on Field-Programmable Technology (ICFPT), 2019Convolutional neural networks (CNNs) are widely used in computer vision applications. GPU has been the mainstream accelerator for CNNs. Compared with GPU, FPGA has the advantages of high flexibility, low power consumption and abundant DSP resources, which make it possible to surpass GPU in some scenarios.
Anrong Yang +6 more
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Multi-Scale YOLOv2 for Hand Detection in Complex Scenes
2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018This paper presents a model named Multi-Scale YOLOv2 (MS-YOLOv2) for hand detection in complex scenes. The proposed MS-YOLOv2 is implemented by introducing three modules to YOLOv2, including a Multi-Scale Feature Refinement Module to acquire fine-grained features, a Channel Importance Evaluation Module to recalibrate feature channels and a Hard Example
Jia Chen, Zihan Ni, Nong Sang
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Healthy and Diseased Tomatoes Detection Based on YOLOv2
2019Disease is one of the key problems that can cause serious yield lost. The effective detection of healthy and diseased tomatoes is of great significance for the development of tomato intelligent farm machinery technologies. This paper analyzed the application of YOLOv2 model on the detection of healthy and diseased tomatoes.
Jiayue Zhao, Jianhua Qu
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