Study of Algorithm for Aerial Target Detection Based on Lightweight Neural Network

Yang, Yumin; Liao, Yurong; Ni, Shuyan; Lin, Cunbao

doi:10.1109/iccece51280.2021.9342470

Cited by 9 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lightweight networks are characterized by a small number of parameters, a simple structure, and fast operation. It is suitable for application to mobile hardware platforms with limited storage and computational resources [24]. SqueezeNet [25] replaces the 3×3 convolution with 1×1 convolution to reduce the parameters.…”

Section: A Mobilenet-v3 Based Yolov4 Network Modelmentioning

confidence: 99%

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

With the development of airdrop technology, the intelligence degree of unmanned powered parachute vehicles (UPPVs) need to be improved. To achieve the accurate landing of UPPVs in complex environments, a landing runway recognition model based on a deep learning algorithm is trained and five actual flight tests are conducted. A six-degree-of-freedom (6-DOF) mathematical model of an unmanned powered parachute vehicle is established, and a landing runway offset controller is designed. The lightweight landing runway recognition model was trained by combining the YOLOv4 framework and the lightweight neural network MobileNet-V3 (Large) and validated in various scenarios. The runway recognition model was transplanted into the airborne image processor, and an unmanned powered parachute vehicle test platform was built for actual flight testing. The test results showed that the comprehensive accuracy of the runway recognition was 97.81% during visual landing and the offset correction was completed within 15s. INDEX TERMS Unmanned powered parachute vehicle; visual landing; YOLOv4; lightweight neural network; offset controller.

show abstract

Section: A Mobilenet-v3 Based Yolov4 Network Modelmentioning

confidence: 99%

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, for real-time detection and lightweight model designs, the channel separation-aggregation structure was developed by Huang et al 15 to simplify the complexity of separable convolutions. Yang et al 16 presented a lightweight RSOD algorithm based on YOLOv3, 8 which selected MobileNetv3 17 as the backbone network. Lang et al 18 designed an ameliorated YOLOv4 9 backbone network that combines with effective channel attention for efficiently extracting features and developed differential evolution to automatically optimize the anchor configuration to solve the problem of large-scale changes in targets.…”

Section: Introductionmentioning

confidence: 99%

“…Lang et al 18 designed an ameliorated YOLOv4 9 backbone network that combines with effective channel attention for efficiently extracting features and developed differential evolution to automatically optimize the anchor configuration to solve the problem of large-scale changes in targets. Although these methods 15,16,18 based on lightweight networks or depthwise separable convolutions have ameliorated the accuracy and speed of the detector to a certain extent, they have not achieved a satisfactory equilibrium between detection accuracy and inference speed. Some algorithms have high detection accuracy but numerous parameters and slow inference, whereas others are the opposite.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AFSPNet: an adaptive feature selection pyramid network for efficient object detection in remote sensing images

Dong

Liu

Gao

et al. 2022

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

In recent studies, remote sensing object detection methods based on deep learning have emerged as a primary concern in environmental monitoring, military investigation, and hazard response. However, many difficulties, such as complex backgrounds, dense target quantities, large-scale variations, and non-uniform distribution, lead to many parameters and complex network structures, thus limiting the accuracy of the detector and slowing the inference speed. To address these issues, we propose a lightweight and efficient object detector for remote sensing images. First, an asymmetric convolution with the visual attention mechanism is reconstructed to decrease the complexity and strengthen the feature representation ability. Then, an adaptive feature selection structure is designed to extract discriminative feature information, which can adaptively model the shapes of objects by introducing deformable convolution to obtain a stronger geometric feature representation. To reduce information loss across different channels and spatial locations, a hybrid receptive field module is also proposed to increase the receptive field model by mixing dilated convolutional layers with different dilation rates. Finally, experimental results on the DIOR dataset show that our approach significantly improves detection accuracy and running speed.

show abstract

LS-YOLO: Lightweight SAR Ship Targets Detection Based on Improved YOLOv5

Wang

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Study of Algorithm for Aerial Target Detection Based on Lightweight Neural Network

Cited by 9 publications

References 3 publications

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

AFSPNet: an adaptive feature selection pyramid network for efficient object detection in remote sensing images

LS-YOLO: Lightweight SAR Ship Targets Detection Based on Improved YOLOv5

Contact Info

Product

Resources

About