Traffic signal image detection technology based on YOLO

Gao, Hui; Wang, Wenhao; Yang, Chengjin; Jiao, Weile; Chen, Ziwei; Zhang, Tong

doi:10.1088/1742-6596/1961/1/012012

Cited by 11 publications

(6 citation statements)

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“…The depthwise separable convolutional network as a feature extractor achieves an average precision of 97.22% during testing. Zhang Sulan et al [10] used the spectral reflectance of Masson pine as the data source to analyze the ridge trace of 14 spectral characteristic parameters and constructed a pine wilt nematode disease ridge regression monitoring model. The test results showed that the determined coefficient R 2 of the constructed pine wilt nematode ridge regression model was 0.8686, the mean square error RMSE was 0.2735, and the average estimation accuracy was 87.15%, which provides technical support for the early monitoring and control of pine wilt nematode disease.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is used to optimize the backbone feature extraction network. Furthermore, the YOLOv4 algorithm was improved by improving the backbone network part, adding CBAM attention, and adding the Inceptionv2 structure to reduce the number of model parameters and improve the accuracy and efficiency of identification. The speed and accuracy of the Faster R-CNN, YOLOv4, SSD, YOLOv5, and the improved MobileNetv2-YOLOv4 algorithm were compared, and the detection effects of the Faster R-CNN, YOLOv4, SSD, YOLOv5 and the improved MobileNetv2-YOLOv4 algorithm on trees with pine wilt nematode were analyzed. The experimental results show that the average precision of the improved MobileNetv2-YOLOv4 algorithm is 86.85%, the training time of each iteration cycle is 156 s, the parameter size is 39.23 MB, and the test time of a single image is 15 ms, which is better than Faster R-CNN, YOLOv4, and SSD, but comparable to YOLOv5. Compared with the advantages and disadvantages, comprehensively comparing these four indicators, the improved algorithm has a more balanced performance in the detection speed, the parameter size, and the average precision. The F1 score of the improved algorithm (95.60%) was higher than that of Faster R-CNN (90.80%), YOLOv4 (94.56%), and SSD (92.14%), which met the monitoring requirements of pine wilt nematode trees. Faster R-CNN and SSD pine-wilt-nematode tree detection models are not ideal in practical applications. Compared with the YOLOv4 pine-wilt-nematode tree detection model, the improved MobileNetv2-YOLOv4 algorithm satisfies the condition of maintaining a lower model parameter quantity to obtain higher detection accuracy; therefore, it is more suitable for practical application scenarios of embedded devices. It can be used for the rapid detection of pine wilt nematode diseased trees.

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Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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“…The alert system is connected to the CPU, which triggers the alarm when animal activity is detected. Data Storage and Management: The system also includes a data storage and management component, which stores all the data captured by the sensors and cameras [9]. The data can be used making, such as identifying patterns of animal activity and optimizing the placement of sensors and cameras.…”

Section: Methodsmentioning

confidence: 99%

Protecting Crops with Real-time Animal Detection Systems

Sharma,

Yadav,

Sharma

et al. 2023

SCRS Proceedings of International Conference of Undergraduate Students 2023

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The protection of crops is a vital aspect of agriculture as it ensures a steady supply of food for the growing population. In recent years, the use of Real-time Animal Detection Systems (RTADS) has gained attention in the agricultural sector. These systems utilize machine-learning algorithms and computer vision techniques to detect and track animals that can cause damage to crops. In this paper, we present a system that aims to develop and evaluate an RTADS for protecting crops from animal damage. The proposed system uses a combination of sensors, cameras, and machine-learning algorithms to detect and track animals in real time. The system is evaluated on a test farm with various types of crops and animals.

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“…Target detection based on deep learning has been a research hot spot over the past few years and has aroused wide attention, 1 and relevant technologies have been applied to face recognition, 2 car driverless, 3,4 pedestrian tracking, 5 intelligent transportation, 6 and other fields. Target detection 7 technology has been found as a key technology of Internet of Things in the livestock industry, which is a vital means to monitor the location, number, health status, as well as whether the livestock are in estrus in real time; this technology also lays a basis for achieving intelligent pasture.…”

Section: Introductionmentioning

confidence: 99%

Enhanced lightweight deep network for efficient livestock detection in grazing areas

Du,

Qi,

Zhu

et al. 2024

International Journal of Advanced Robotic Systems

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There are problems in the special pastoral environment, including large changes in target size and serious interference from light and environmental factors. To solve the above problems, an enhanced YOLOv4-tiny target detection network is proposed in this study. This network first solves the problem of livestock size fluctuation in pastoral areas, uses a pyramid network with multiscale feature fusion, and considers shallow local detail features and deep semantic information. Subsequently, a novel compound multichannel attention mechanism is proposed to increase the accuracy of the target detection network for the pastoral environment. The problem of poor accuracy of target detection network is solved. The algorithm is ported to Jetson AGX embedded platform for validation to examine the real-time performance of the algorithm. As revealed by the experimental results, enhanced YOLOv4-tiny achieves 89.77% detection accuracy and 30 frames/second detection speed, which increases the average detection accuracy by 11.67% compared with the conventional YOLOv4-tiny while maintaining almost the same detection rate.

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Traffic signal image detection technology based on YOLO

Cited by 11 publications

References 0 publications

Detection of Pine Wilt Nematode from Drone Images Using UAV

Detection of Pine Wilt Nematode from Drone Images Using UAV

Protecting Crops with Real-time Animal Detection Systems

Enhanced lightweight deep network for efficient livestock detection in grazing areas

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