WebApr 28, 2024 · 3.1 Network Structure. In this paper, we propose a lightweight target detection model based on YOLOv4. We use lightweight convolutional neural networks and deep separable convolutions to reduce the amount of model parameters, and combine Coordinate attention (collaborative attention mechanism) and ASFF (adaptive spatial … WebJul 10, 2024 · Experiments conducted at MS-COCO show that the proposed CSL-Module can approximate the fitting ability of Convolution-3x3. Finally, we use the module to …
Exploring the power of lightweight YOLOv4 - computer.org
WebOct 15, 2024 · Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise … WebOct 17, 2024 · Exploring the power of lightweight YOLOv4. Abstract: Research on deep learning has always had two main streams: (1) design a powerful network architecture … cyber security postcolonialism
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WebIn this work, we set a very ambitious goal of exploring the power of lightweight neural networks. We utilize the analysis of data space, model’s representational capacity, and … WebOct 6, 2024 · I'm trying to use YOLO to detect license plate in an Android application. So I train a YOLOv3 and a YOLOv4 model in Google Colab. I converted these 2 models to … WebApr 20, 2024 · This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. ... The Tiny-YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of … cyber security possibility effect