LES-YOLO: A lightweight pinecone detection algorithm based on improved YOLOv4-Tiny network

Cui, Mingdi; Lou, Yunyi; Ge, Yilin; Wang, Keqi

doi:10.1016/j.compag.2023.107613

Cited by 31 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, aerial UAVs have been widely used in many fields, including power inspection [10], rail transportation [11], agricultural production [12] and disaster monitoring [13], to extract object features or targets of interest through target detection by aerial video captured by UAVs [14].…”

Section: Related Workmentioning

confidence: 99%

“…In agricultural scenes, drones are commonly applied for production operations, and drones with vision functions are generally applied for disease detection [12]; in the field of electric power or rail transportation inspection, the scene depth is large, and drones and fixed aircraft positions are commonly used in cooperation, and drones are generally applied for fine operations with relatively low requirements for real-time, and the underground platform of the open pit mine has a large drop from the ground and a complex slope structure. The safety monitoring of mining trucks is very necessary, which is beneficial to the safety production work of open pit coal mines [5].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

Wu¹,

Wang²,

Ni³

2023

OJAppS

View full text Add to dashboard Cite

In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV. The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment. A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed. Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

Wu¹,

Wang²,

Ni³

2023

OJAppS

View full text Add to dashboard Cite

show abstract

“…Recently, YOLO has been adopted for various agriculture detection applications such as plant organs detection [ 44 ], Tea chrysanthemum detection [ 30 ], date palm tree detection from drone imagery [ 13 ], tea leaf detection [ 4 ], tomato detection [ 53 ], pinecone detection [ 6 ], fruit detection [ 43 ]. In [ 44 ], ResNet and DenseNet backbone feature extractor networks were utilised to train YOLO-v3 for plant orangutan identification.…”

Section: Related Workmentioning

confidence: 99%

“…932 pictures were captured in total. 95% [ 6 ] 2023 Cui et al YOLOv4-Tiny A total of 1200 photos of pinecones were obtained from a forest farm in the Chinese province of Heilongjiang. 95.3% [ 43 ] 2023 Tang et al improved YOLOv4-tiny 1600 images for training and 400 for evaluation 92% [ 32 ] 2023 Qiu et al YOLOv4-tiny A dataset of 1000 photographs was randomly used as training, while the remaining 200 images were collected as test data.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning vision system for human action recognition from drone images

Samma

Sama

2023

Multimed Tools Appl

View full text Add to dashboard Cite

There are several benefits to constructing a lightweight vision system that is implemented directly on limited hardware devices. Most deep learning-based computer vision systems, such as YOLO (You Only Look Once), use computationally expensive backbone feature extractor networks, such as ResNet and Inception network. To address the issue of network complexity, researchers created SqueezeNet, an alternative compressed and diminutive network. However, SqueezeNet was trained to recognize 1000 unique objects as a broad classification system. This work integrates a two-layer particle swarm optimizer (TLPSO) into YOLO to reduce the contribution of SqueezeNet convolutional filters that have contributed less to human action recognition. In short, this work introduces a lightweight vision system with an optimized SqueezeNet backbone feature extraction network. Secondly, it does so without sacrificing accuracy. This is because that the high-dimensional SqueezeNet convolutional filter selection is supported by the efficient TLPSO algorithm. The proposed vision system has been used to the recognition of human behaviors from drone-mounted camera images. This study focused on two separate motions, namely walking and running. As a consequence, a total of 300 pictures were taken at various places, angles, and weather conditions, with 100 shots capturing running and 200 images capturing walking. The TLPSO technique lowered SqueezeNet’s convolutional filters by 52%, resulting in a sevenfold boost in detection speed. With an F1 score of 94.65% and an inference time of 0.061 milliseconds, the suggested system beat earlier vision systems in terms of human recognition from drone-based photographs. In addition, the performance assessment of TLPSO in comparison to other related optimizers found that TLPSO had a better convergence curve and achieved a higher fitness value. In statistical comparisons, TLPSO surpassed PSO and RLMPSO by a wide margin.

show abstract

“…Bhagat et al [24] proposed a lightweight WheatNet-Lite architecture for wheat spikes detection, which integrated Mixed Depthwise Conv (MDWConv), Modified Spatial Pyramidal Polling (MSPP), and Depthwise Convolution (DWConv), with 54.2 M fewer network parameters compared with YOLOv3. Zha et al [25] proposed YOLOv4_MF model for pest detection, using MobileNetV2 as a feature extraction block to reduce model parameters and focus loss instead of cross-entropy loss, and designing an improved feature fusion structure, finally the mean average precision (mAP) of the model was 4.24% higher than YOLOv4, while the volume was reduced to 1/6 of YOLOv4. Cui et al [26] proposed a YOLOv4-Tiny model for pine cone detection, using LESNet as the backbone to extract pine cone features and a feature pyramid network with SE attention to fuse multi-scale information, and the average precision of the improved model was improved by 56.4% over the original, and the parameters and computation were compressed to 12.22% and 17.35% of the original network, respectively.…”

Section: Introductionmentioning

confidence: 99%

Lightweight detection method for lotus seedpod in natural environment

Tang,

Wang,

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

In order to solve the problems of the current target detection algorithms, such as poor discrimination of occluded targets, multiple parameters, complex networks, large amounts of computation, and not conducive to the deployment of mobile terminals, a lightweight lotus seedpod detection method based on YOLOv5s model was proposed in this study. First, the dataset was augmented by using a combination of offline and online augmentation, which improved the adaptability and robustness of the model in complex environments. Then, a lightweight Ghost convolution module was introduced to replace the original convolution, and a lightweight bidirectional feature pyramid network was designed, which could enhance the feature extraction and fusion capability of the network and reduce the amount of calculation and model size; On this basis, the combination of WIoU loss function and Mish activation function was adopted to improve the accuracy of feature extraction. Finally, the knowledge distillation training strategy was used to ensure the proposed lightweight model has the learning ability of a complex network model, improving the recall and precision of model detection. The results of the ablation study show that the proposed method effectively improves the detection performance of the YOLOv5s model for lotus seedpods. The mean average precision of the improved model was 89.7%, compared with the original YOLOv5s model increased by 2.8%, and the parameters and FLOPs were reduced by 2.36M and 7.3G, respectively. Compared with other detection algorithm models, the proposed algorithm model has the advantages of less computation, smaller model size, and higher detection precision. Therefore, the proposed improved optimization method based on the YOLOv5s model can effectively detect lotus seedpods, which provides theoretical research and technical support for intelligent picking of lotus seedpods in the actual operating environment.

show abstract

LES-YOLO: A lightweight pinecone detection algorithm based on improved YOLOv4-Tiny network

Cited by 31 publications

References 27 publications

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

Optimized deep learning vision system for human action recognition from drone images

Lightweight detection method for lotus seedpod in natural environment

Contact Info

Product

Resources

About