Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot

Ge, Yuhao; Lin, Sen; Zhang, Yunhe; Li, Zuo‐Lin; Cheng, Hongtai; Dong, Jing; Shao, Shuai; Zhang, Jin; Qi, Xiangyu; Wu, Zedong

doi:10.3390/machines10060489

Cited by 44 publications

(16 citation statements)

References 44 publications

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“…Yang et al [ 19 ] added a self-attentive module to YOLO V4 to improve the accuracy of counting wheat ears. Ge et al [ 20 ] made a series of improvements to YOLO V5s and proposed YOLO-Deepsort, thus tracking and counting tomatoes at different growth periods. YOLO X is one of the latest achievements of the YOLO series and it performs better than the previous YOLO model [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting

xiang

Wang

et al. 2023

Plant Methods

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Background The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult. Results We propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The results showed that the R2 between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. Moreover, MAE, MAPE, RMSE are only 4.18, 10.0%, 6.48 respectively, the deviation is very small. Conclusions We have achieved the first accurate counting of soybean pods and proposed a new solution for the detection and counting of dense objects.

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Section: Introductionmentioning

confidence: 99%

YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting

xiang

Wang

et al. 2023

Plant Methods

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“…In YOLOv5s, loss functions consist of three losses: confidence loss (I obj ), classification loss (I cls ), and position loss of the target box represented by prediction box loss (I box ). The loss equation is as follows: Loss = I obj + I cls + I box (12) In YOLOv5s, the Generalized Intersection over Union (GIoU) loss function is the default bounding box regression loss (I box ) function [23]. Compared to the IoU loss function regressor, GIoU solves differentiability issues when the prediction box and target box do not intersect, i.e., IoU = 0, and when the intersection of two prediction boxes has the same size and same IoU.…”

Section: Siou Loss Functionmentioning

confidence: 99%

“…The detection model displayed improvement in mAP and F1-score by 9.83% and 6.49%, respectively, over the YOLOv5 base model. He et al [11] suggested a YOLOv4-tiny model to detect different maturity levels of strawberries that outperformed the standard YOLOv4 model with an increment in mAP by 5.77% and a decrease in inference time of 51.01 ms. Ge et al [12] developed an improved YOLOv5s model named YOLO-DeepSort that replaces the original backbone with a shufflenetv2 and added an attention module to better identify and count flowers, green tomatoes, and red tomatoes during growth phases. The improved model outperformed base YOLOv5 in precision in identifying flower, green, and red tomatoes by 17%, 2%, and 2.3%, respectively, with a decreased model size of 10.5 MB.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

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A lightweight strawberry detection and localization algorithm plays a crucial role in enabling the harvesting robot to effectively harvest strawberries. The YOLO model has often been used in strawberry fruit detection for its high accuracy, speed, and robustness. However, some challenges exist, such as the requirement for large model sizes, high computation operation, and undesirable detection. Therefore, the lightweight improved YOLOv5s-CGhostnet was proposed to enhance strawberry detection. In this study, YOLOv5s underwent comprehensive model compression with Ghost modules GCBS and GC3, replacing modules CBS and C3 in the backbone and neck. Furthermore, the default GIOU bounding box regressor loss function was replaced by SIOU for improved localization. Similarly, CBAM attention modules were added before SPPF and between the up-sampling and down-sampling feature fusion FPN–PAN network in the neck section. The improved model exhibited higher mAP@0.5 of 91.7% with a significant decrement in model size by 85.09% and a reduction in GFLOPS by 88.5% compared to the baseline model of YOLOv5. The model demonstrated an increment in mean average precision, a decrement in model size, and reduced computation overhead compared to the standard lightweight YOLO models.

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“…Osman et al [ 20 ] performed the dynamic tracking and counting of apples with an accuracy of 91% and performed the good tracking of targets with restricted fields of view under occlusion and static conditions. Ge et al [ 21 ] detected and tracked tomatoes during different periods, including flowering, green, and red tomatoes with accuracy values of 93.1%, 96.4%, and 97.9%, respectively. Zheng et al [ 22 ] video filmed citrus fruits by UAV, and counted the citrus appearing in the video with an F1 score of 89.07% by combining the YOLO and DeepSort models.…”

Section: Introductionmentioning

confidence: 99%

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

Zhong

Chen

et al. 2023

Sensors

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The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7–DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.

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Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot

Cited by 44 publications

References 44 publications

YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting

YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort

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