A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s

Yang, Wenhan; Liu, Tianyu; Jiang, Ping; Qi, Aolin; Deng, Lexing; Liu, Zelong; He, Yuchen

doi:10.3390/ani13193134

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…It consists of Focus, Conv, C3, and SPP, and is responsible for extracting features from multi-scale images. Compared to other models, the C3 module effectively reduces the repetition of gradient information during network information transmission [42]. By adjusting the number and depth of C3 modules, the total number of parameters in the model can be controlled.…”

Section: Wl-yolomentioning

confidence: 99%

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

Ma,

Dong,

Xia

et al. 2024

Remote Sensing

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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.

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Section: Wl-yolomentioning

confidence: 99%

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

Ma,

Dong,

Xia

et al. 2024

Remote Sensing

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“…In the neck enhancement feature extraction networks of the YOLOv5 model, the structure of the Feature Pyramid Network (FPN) and Path Aggregation Network (PANet) is utilized to combine shallow graphical features with deep semantic features [23], as depicted in Figure 7. Specifically, the FPN fuses low-level features by upsampling them to the toplevel features and makes predictions on each fused feature layer.…”

Section: The Yolov5 Algorithmmentioning

confidence: 99%

A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm

Xiao,

Wang,

et al. 2023

Agriculture

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In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall (R) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices.

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“… Lou et al (2023) proposed a small-size object detection algorithm based on YOLOv8 model for special scenes, which improved the detection accuracy of small-size objects. Yang et al (2023) proposed a method for real-time detection of forest fires based on YOLOv5 network, the mAP increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Zhang et al (2022) proposed an improved model based on YOLOv5 and used it to detect orchard pests, the results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5.…”

Section: Introductionmentioning

confidence: 99%

An improved lightweight object detection algorithm for YOLOv5

Luo,

Wei,

Wang

et al. 2024

PeerJ Computer Science

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Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model’s inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model’s ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling—Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

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A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s

Cited by 9 publications

References 44 publications

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

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

A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm

An improved lightweight object detection algorithm for YOLOv5

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