LOCAL-IN-TIME SOLVABILITY OF TARGET DETECTION MODEL IN MOLECULAR COMMUNICATION NETWORK

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

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Epidemic Information Dissemination in Mobile Molecular Communication Networks for Target Detection Applications

Proceedings of the 10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) ◽

10.4108/eai.22-3-2017.152408 ◽

2017 ◽

Author(s):

Yutaka Okaie ◽

Tadashi Nakano ◽

Takahiro Hara

Keyword(s):

Communication Networks ◽

Target Detection ◽

Information Dissemination ◽

Molecular Communication

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Bacterial colonies detecting and counting based on enhanced CNN detection method

E3S Web of Conferences ◽

10.1051/e3sconf/202123302012 ◽

2021 ◽

Vol 233 ◽

pp. 02012

Author(s):

Shousheng Liu ◽

Zhigang Gai ◽

Xu Chai ◽

Fengxiang Guo ◽

Mei Zhang ◽

...

Keyword(s):

Target Detection ◽

Error Detection ◽

Detection Rate ◽

Detection Method ◽

Difficult Problem ◽

Detection Methods ◽

Detection Accuracy ◽

Detection Model ◽

Confidence Threshold ◽

Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

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