A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s

Liu, Kaixuan; Wang, Jie; Zhang, Kai; Chen, Minhui; Zhao, Haonan; Liao, Juan

doi:10.3390/s23156738

Cited by 6 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SE attention mechanism comprises compression and excitation parts, involving two fully connected layers with Relu6 and h − swish activation functions, respectively, after global average pooling of features [44,45]. The original authors approximated the swish activation function with ReLU6 to create the h − swish activation function, which effectively addresses the issue of complex gradient calculation [46,47]. The computation formula for the h-swish activation function is as follows:…”

Section: Mobilenetv3 Classificationmentioning

confidence: 99%

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Wang,

Chen,

2023

Sensors

View full text Add to dashboard Cite

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of “detection first, then removal” to overcome shadow interference and instead employs a “segmentation-classification” approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements.

show abstract

Section: Mobilenetv3 Classificationmentioning

confidence: 99%

Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows

Wang,

Chen,

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Kaixuan Liu et al developed an algorithm specifically for quickly identifying the rice fertility period. Building upon the lightweight nature of YOLOv5s [33], the backbone network was replaced with MobileNetV3 to enhance the model's detection speed. Additionally, the feature extraction network was replaced with GSConv to reduce the computational costs, and a lightweight Neck network was constructed to decrease the complexity of the model while preserving accuracy.…”

Section: Introductionmentioning

confidence: 99%

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

Ma,

Dong,

Xia

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model.

show abstract