AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning

Shao, Yanhua; Zhang, Xingping; Chu, Hsin‐Sen; Zhang, Xiaoqiang; Zhang, Duo; Rao, Yunbo

doi:10.3390/app12073627

Cited by 21 publications

(12 citation statements)

References 30 publications

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“…The YOLO algorithm was used by Shao et al [11] for the detection of pedestrians when using UAVs (similar to this research). The authors achieved an AP (average precision) of 93.2% for YOLOv3, while mask R-CNN achieved 95.5%; the method based on YOLOv3, called AIR-YOLOv3, reached 91.5%.…”

Section: Yolo Algorithmmentioning

confidence: 98%

Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

et al. 2022

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This research describes an experimental solution used for estimating the positions of pedestrians from video recordings. Additionally, clustering algorithms were utilized to interpret the data. The system employs the You Only Look Once (YOLO) algorithm for object detection. The detection algorithm is applied to video recordings provided by an unmanned aerial vehicle (UAV). An experimental method for calculating the pedestrian’s geolocation is proposed. The output of the calculation, i.e., the data file, can be visualized on a map and analyzed using cluster analyses, including K-means, DBSCAN, and OPTICS algorithms. The experimental software solution can be deployed on a UAV or other computing devices. Further testing was performed to evaluate the suitability of the selected algorithms and to identify optimal use cases. This solution can successfully detect groups of pedestrians from video recordings and it provides tools for subsequent cluster analyses.

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Section: Yolo Algorithmmentioning

confidence: 98%

Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

et al. 2022

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“…It is easy to achieve a trade-off between performance and parameters by adjusting the pruning rate. At the same time, layer-wise pruning is often used as an auxiliary pruning method in networks with many branches [85], [86]. Although the channel/filter-wise pruning based on reinforcement learning is time-consuming, it effectively solves the limitation of manually set magnitudes [70].…”

Section: Network Pruningmentioning

confidence: 99%

A Review of AIoT-based Edge Devices and Lightweight Deployment

Sun¹,

Wang²,

Zhao³

2023

Preprint

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<p>Artificial Intelligence of Things (AIoT) which inte?grates artificial intelligence (AI) and the Internet of Things (IoT), has attracted increasing attention recently. With the remarkable development of AI, deep neural networks (DNNs) have achieved great success from research to deployment in many applications. However, deploying complex and state-of-the-art (SOTA) AI models on edge applications is increasingly a big challenge. This paper focuses on the convergence of AIoT-based edge devices, lightweight DNNs, and neural network compression. We provide a comprehensive analysis of them and many practical suggestions for researchers: how to obtain/design lightweight DNNs, how to select suitable AI edge devices, and how to compress and deploy them in practice. Finally, future trends and opportunities are presented, including trustworthy AI, federated learning, and robust deployment. </p>

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“…Shao et al also proposed a method combining network pruning and YOLOv3 for aerial IR pedestrian detection; Smooth-L1 regularization is introduced on the channel scale factor; This speeds up reasoning while ensuring detection accuracy. Compared with the original YOLOv3, the model volume is compressed by 228.7 MB, and the model AP is reduced by only 1.7% [ 43 ]. The above method can effectively simplify the model while ensuring image quality and detection accuracy, providing a reference for the development of portable mobile terminals.…”

Section: Sheep Face Recognition Based On Yolov3-pmentioning

confidence: 99%

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

Song

Liu

Wang

et al. 2022

Animals

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Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.

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AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning

Cited by 21 publications

References 30 publications

Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

A Review of AIoT-based Edge Devices and Lightweight Deployment

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

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