DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection

Wang, Yan; Yan, Gang; Meng, Qinglu; Yao, Ting; Han, Jianfeng; Bo, Zhang

doi:10.1016/j.compag.2022.107057

Cited by 55 publications

(21 citation statements)

References 18 publications

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“…Similarly, the transient maturity class exhibits the least mAP value due to the wide feature space between immature and mature classes, compounded by the limited number of data samples in the dataset. A similar observation was observed in the studies of SDNet [25] and DSE-YOLO model [10].…”

Section: Analysis Of Class-wise Strawberry Fruit Detection Algorithmssupporting

confidence: 88%

“…However, its larger model size at 54.6 MB and a moderate FPS of 30.5 are of concern. Conversely, the DSE-YOLO model [10] exhibits the lowest accuracy with a mAP@0.5 of 86.58, accompanied by a smaller model size of 22.39 MB but a slower FPS of 18.20. YOLOv8, incorporating the fused LW-Swin Transformer [26], demonstrates competitive performance with a mAP@0.5 of 94.4 and a significantly reduced model size of 13.42 MB, albeit at a lower FPS of 19.23.…”

Section: Comparison With Relevant Studiesmentioning

confidence: 99%

“…The lightweight model is realized using techniques such as low-rank decomposition of dense convolution kernels, model pruning, knowledge distillation, and parameter quantification [9]. Wang et al [10] proposed an improved YOLOv3 model with detailed semantic features extraction to classify different fruit maturities and a modified loss function to detect small-size fruits. The detection model displayed improvement in mAP and F1-score by 9.83% and 6.49%, respectively, over the YOLOv5 base model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Analysis Of Class-wise Strawberry Fruit Detection Algorithmssupporting

confidence: 88%

Section: Comparison With Relevant Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

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“…Detection performance was significantly improved in small target detection. Wang et al [25] proposed a DSE-YOLO model with good results based on YOLOv3 for strawberry detection. However, the detection of YOLOv3 is not satisfactory for targets with complex features.…”

Section: Target Detection Algorithmsmentioning

confidence: 99%

Research on Forest Fire Detection Algorithm Based on Improved YOLOv5

Lian

2023

MAKE

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Forest fires are one of the world’s deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding an attention mechanism to the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection performance of the model and the robustness of the model. In addition, a random mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model processing load and to improve the detection speed. Experimental results on self-built forest-fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.

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“…However, there is still room for further optimization in detection accuracy. The YOLO (You Only Look Once) family of algorithms belongs to the One-Stage family of algorithms and does not have a process for generating bounding boxes [ 15 ], which can guarantee the real-time nature of the model and thus are widely used in the agricultural field [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Because embedded devices often do not have a large memory, the smaller the model, the easier it is to deploy to embedded devices.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Duck Egg Hatching Characteristics with a Lightweight Multi-Target Detection Method

Zhou

Liu

Zhou

et al. 2023

Animals

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Since it is difficult to accurately identify the fertilization and infertility status of multiple duck eggs on an incubation tray, and due to the lack of easy-to-deploy detection models, a novel lightweight detection architecture (LDA) based on the YOLOX-Tiny framework is proposed in this paper to identify sterile duck eggs with the aim of reducing model deployment requirements and improving detection accuracy. Specifically, the method acquires duck egg images through an acquisition device and augments the dataset using rotation, symmetry, and contrast enhancement methods. Then, the traditional convolution is replaced by a depth-wise separable convolution with a smaller number of parameters, while a new CSP structure and backbone network structure are used to reduce the number of parameters of the model. Finally, to improve the accuracy of the network, the method includes an attention mechanism after the backbone network and uses the cosine annealing algorithm in training. An experiment was conducted on 2111 duck eggs, and 6488 duck egg images were obtained after data augmentation. In the test set of 326 duck egg images, the mean average precision (mAP) of the method in this paper was 99.74%, which was better than the 94.92% of the YOLOX-Tiny network before improvement, and better than the reported prediction accuracy of 92.06%. The number of model parameters was only 1.93 M, which was better than the 5.03 M of the YOLOX-Tiny network. Further, by analyzing the concurrent detection of single 3 × 5, 5 × 7 and 7 × 9 grids, the algorithm achieved a single detection number of 7 × 9 = 63 eggs. The method proposed in this paper significantly improves the efficiency and detection accuracy of single-step detection of breeder duck eggs, reduces the network size, and provides a suitable method for identifying sterile duck eggs on hatching egg trays. Therefore, the method has good application prospects.

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DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection

Cited by 55 publications

References 18 publications

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

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

Research on Forest Fire Detection Algorithm Based on Improved YOLOv5

Evaluation of Duck Egg Hatching Characteristics with a Lightweight Multi-Target Detection Method

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