Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model

Xu, Li; Pan, Jiandong; Xie, Fangping; Zeng, Jinshan; Qiao, Lingling; Huang, Xiaojun; Liu, Dawei; Wang, Xiushan

doi:10.1016/j.compag.2021.106503

Cited by 72 publications

(42 citation statements)

References 19 publications

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“…Figure 8e shows the P-R curve of five images during the detection process, which are randomly captured to calculate the value of AP; the results are 97.18%, 86.11%, 96.59%, 95.42%, and 94.51%, and the fps is capped at about five frames per second. In the report of Li et al [10], the green pepper detection algorithm, based on an improved yolov4-tiny model, is 95.11%, and the frame rate is 89 FPS; the difference is mainly reflected in the size of the model. The detection algorithm proposed in this paper has a smaller volume, so it has a fast calculation speed and suitable for the real-time requirements of a vehiclemounted environment.…”

Section: Field Experiments Resultsmentioning

confidence: 99%

“…Suo et al [8] used yolov4 to study the transfer of kiwifruit detection, and obtained the highest mAP of 91.9% with an image processing speed of 25.5 ms. Zhang et al [9] proposed a water-meter pointerreading recognition method based on improved yolov4; the detection accuracy of this method reached 98.68%, which indicated that the lightweight algorithm could quickly and accurately identify targets. Li et al [10] proposed a rapid detection model for green pepper based on yolov4-tiny, the average precision is 95.11%, the model size is 30.9 MB, and the frame rate is 89 FPS. The CNN algorithms used in this research have reached a high level of accuracy in identifying crop characteristics, which can provide a good research basis for the detection of broken corn kernels.…”

Section: Introductionmentioning

confidence: 99%

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Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Yao

et al. 2021

Agriculture

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At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

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Section: Field Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Yao

et al. 2021

Agriculture

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“…With the expansion of the number of various public datasets and the development of image processing and object detection technologies, the research on leaf disease feature location and classification based on field crop images has developed rapidly [19][20][21][22][23]. The first step of the detection process is the localization of the disease, which is mainly based on the feature information extracted by the model to locate the disease spots and judge the degree of infection.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

et al. 2022

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Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

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“…The models of deep learning trained are applied to identify and detect the sequence of captured images 58 , 61 – 69 . And the algorithm is used to calculate the direction speed of the target and the distance to provide data for the next step.…”

Section: System Modelmentioning

confidence: 99%

Study on a risk model for prediction and avoidance of unmanned environmental hazard

Qiu

Zhang

et al. 2022

Sci Rep

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Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, according to the risk prediction results, a conservative driving strategy based on speed limit is adopted. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of imbalanced sample categories. Software such as MATLAB and CarSim are applied in the system. Bleu-1 was 67.1, bleu-2 was 45.1, bleu-3 was 29.9 and bleu-4 was 21.1. Experiments were carried out on the database flickr30k by designing the algorithm. Bleu-1 was 72.3, bleu-2 was 51.8, bleu-3 was 37.1 and bleu-4 was 25.1. From the comparison results of the simulations of unmanned vehicles with or without a system, it can provide effective safety guarantee for unmanned driving.

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Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model

Cited by 72 publications

References 19 publications

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Study on a risk model for prediction and avoidance of unmanned environmental hazard

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