Fourier Domain Pruning of MobileNet-V2 with Application to Video Based Wildfire Detection

Pan, Hongyi; Badawi, Diaa; Çetin, A. Enis

doi:10.1109/icpr48806.2021.9412613

Cited by 5 publications

(1 citation statement)

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“…The complexity of generating an adversarial network is too high to meet the needs of UAV aerial image target detection. Therefore, some researchers advocated combining lightweight network models such as the MobileNet [31][32][33] series, ShuffleNet [34,35] series, and others to be used in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

Zhang¹,

Deng²,

Chen³

2023

Computers, Materials &Amp; Continua

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In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and better retain important feature information.(2) The feature information is enriched by improving the multi-scale feature pyramid structure and strengthening the feature fusion at different scales. (3) A new loss function is created and the diagonal penalty term of the anchor frame is introduced to improve the speed of training and the accuracy of reasoning. The proposed model is called residual feature fusion triple attention YOLO (RT-YOLO). Experiments showed that the mean average precision (mAP) of RT-YOLO is increased from 57.2% to 60.8% on the vehicle detection in aerial image (VEDAI) dataset, and the mAP is also increased by 1.7% on the remote sensing object detection (RSOD) dataset. The results show that the RT-YOLO outperforms other mainstream models in UAV aerial image object detection.

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