Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny

Ma, Li; Zhao, Liang; Wang, Zixuan; Zhang, Jian; Chen, Guifen

doi:10.3390/agronomy13051419

Cited by 39 publications

(11 citation statements)

References 44 publications

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“…Regarding practical deployment application, the YOLO series of algorithms have demonstrated relatively good deployment effectiveness across multiple areas [42][43][44],…”

Section: Discussionmentioning

confidence: 99%

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Yao,

Lin,

Bai

et al. 2024

Forests

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Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model’s focus on the TLB’s small targets and optimize detection accuracy. Furthermore, the model’s loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation.

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“…Regarding practical deployment application, the YOLO series of algorithms have demonstrated relatively good deployment effectiveness across multiple areas [42][43][44],…”

Section: Discussionmentioning

confidence: 99%

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Yao,

Lin,

Bai

et al. 2024

Forests

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“…Compared with second-order target detection algorithms, such as Faster R-CNN, R-CNN, and Fast R-CNN, YOLOv7 adopts first-order regression and obtains the location and category of the corresponding target through convolutional operation at one time with fast detection speed and a better balance between speed and accuracy [ 29 , 30 ]. In addition, unlike first-order algorithms, such as YOLOv5, SSD, and CenterNet, YOLOv7 adopts faster convolutional operation and a more innovative network structure, making it popular in target detection [ 31 ]. However, there is still some room for improvement and optimization of YOLOv7 for small-target detection, especially for the detection of small-target floating garbage with frequent morphological changes.…”

Section: Methodsmentioning

confidence: 99%

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

Jiang,

Wu,

et al. 2023

Sensors

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As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

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“…The YOLOv7-tiny-Apple model, which has been proposed as a lightweight smalltarget apple recognition and counting tool, can be used for autonomous orchard management, assisting in real-time apple detection and more efficient orchard management by identifying and counting apples [20]. The model provides theoretical support for developing apple identification and counting models by providing new insights on hardware installations and orchard yield estimation.…”

Section: Introductionmentioning

confidence: 99%

A Seamless Deep Learning Approach for Apple Detection, Depth Estimation, and Tracking Using YOLO Models Enhanced by Multi-Head Attention Mechanism

Sekharamantry,

Melgani,

Malacarne

et al. 2024

Computers

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Considering precision agriculture, recent technological developments have sparked the emergence of several new tools that can help to automate the agricultural process. For instance, accurately detecting and counting apples in orchards is essential for maximizing harvests and ensuring effective resource management. However, there are several intrinsic difficulties with traditional techniques for identifying and counting apples in orchards. To identify, recognize, and detect apples, apple target detection algorithms, such as YOLOv7, have shown a great deal of reflection and accuracy. But occlusions, electrical wiring, branches, and overlapping pose severe issues for precisely detecting apples. Thus, to overcome these issues and accurately recognize apples and find the depth of apples from drone-based videos in complicated backdrops, our proposed model combines a multi-head attention system with the YOLOv7 object identification framework. Furthermore, we provide the ByteTrack method for apple counting in real time, which guarantees effective monitoring of apples. To verify the efficacy of our suggested model, a thorough comparison assessment is performed with several current apple detection and counting techniques. The outcomes adequately proved the effectiveness of our strategy, which continuously surpassed competing methods to achieve exceptional accuracies of 0.92, 0.96, and 0.95 with respect to precision, recall, and F1 score, and a low MAPE of 0.027, respectively.

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Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny

Cited by 39 publications

References 44 publications

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

A Seamless Deep Learning Approach for Apple Detection, Depth Estimation, and Tracking Using YOLO Models Enhanced by Multi-Head Attention Mechanism

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