LCNet: A Light-Weight Network for Object Counting

Yu, Houshun; Zhang, Li

doi:10.1007/978-3-030-63830-6_35

Cited by 4 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the proposed method begins with the preprocessing process. Next, a modeling process was carried out for each of the models used, namely EfficientNet [11], EfficientNetV2 [12], LCNet [13], MobileNetV3 [14], TinyNet [15], and FBNetV3 [16]. The six models are used because previous research conducted by Yi et al [7] is an improvement to the EfficientNet model by adding Residual Attention, which is an attention mechanism in its architecture.…”

Section: Methodology Figure 1 Workflow Of the Proposed Diabetic Retin...mentioning

confidence: 99%

“…Given that APTOS2019 [7] dataset gave insufficient and unbalanced datasets, it is more difficult to obtain a satisfactory result based on deep learning technology alone. To solve this problem, we adopted transfer learning technology in our model, by experimenting using six different architecture that is EfficientNet [11], EfficientNetV2 [12], LCNet [13], MobileNetV3 [14], TinyNet [15], and FBNetV3 [16]. Those architectures were already pre-trained on ImageNet and gives a satisfactory classification performance.…”

Section: Modellingmentioning

confidence: 99%

“…In this paper, our experiment uses APTOS2019 [7] dataset to classify the severity of diabetic retinopathy. We experimented using six different deep learning architectures such as EfficientNet [11], EfficientNetV2 [12], LCNet [13], MobileNetV3 [14], TinyNet [15], and FBNetV3 [16] then combining the top four models that give the best validation accuracy, using ensemble stacking method. We also experimented to try different meta-learner algorithms such as logistic regression, SVM, decision tree, and ANN.…”

Section: Experimental Designmentioning

confidence: 99%

“…In this study, EfficientNet [11] was used because the architecture was proven to provide good accuracy in classifying the severity of diabetes retinopathy by previous researchers [9]. This research also uses other architectures such as EfficientNet [11], EfficientNetV2 [12], LCNet [13], MobileNetV3 [14], TinyNet [15], and FBNetV3 [16] because the architecture is proven to have better accuracy in classifying images in the ImageNet dataset. In addition, these models have not previously been used for cases of diabetic retinopathy severity classification.…”

Section: Introductionmentioning

confidence: 99%

“…1. Evaluate the latest models whose architecture uses attention mechanisms in classifying the severity of diabetes retinopathy namely, EfficientNet [11], EfficientNetV2 [12], LCNet [13], MobileNetV3 [14], TinyNet [15], and FBNetV3 [16].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Handoyo¹,

Kusuma²

2022

RIA

View full text Add to dashboard Cite

Diabetic retinopathy (DR), is a complication resulting from the disease that can lead to blindness if not detected early. Recently, many classification systems for diabetic retinopathy have been developed. However, several problems were found, namely, the classification results in certain classes still have less than optimal accuracy values, the lack of in-depth analysis for the results, and the overall accuracy that can still be improved. In this work, we experiment by evaluating and combining new deep learning models such as EfficientNet, EfficientNetV2, LCNet, MobileNetV3, TinyNet, and FBNetV3 using ensemble stacking techniques with four different meta-learners: decision trees, logistic regression, ANN, and SVM to provide better accuracy in classifying the severity of diabetic retinopathy. Our work offers satisfactory classification results on the APTOS 2019 dataset with training, validation, testing, and F1 score accuracy of 96.56%, 95.33%, 84.17%, and 70.16%, respectively.

show abstract

Section: Methodology Figure 1 Workflow Of the Proposed Diabetic Retin...mentioning

confidence: 99%

Section: Modellingmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Handoyo¹,

Kusuma²

2022

RIA

View full text Add to dashboard Cite

show abstract

SCU-Counting: A large-scale benchmark dataset for multi-class object counting

Wei,

Zhang,

et al. 2024

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

show abstract

LCNet: A Light-Weight Network for Object Counting

Cited by 4 publications

References 21 publications

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method

SCU-Counting: A large-scale benchmark dataset for multi-class object counting

A review of intelligent ship marine object detection based on RGB camera

Contact Info

Product

Resources

About