SE-YOLOv4: shuffle expansion YOLOv4 for pedestrian detection based on PixelShuffle

Liu, Mingsheng; Wan, Liang; Wang, Bo; Wang, Tingting

doi:10.1007/s10489-023-04456-0

Cited by 10 publications

(21 citation statements)

References 44 publications

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“…Zhang et al [33] proposed using K-means clustering in the training set to find the best prior and improve detection accuracy. Liu et al [34] proposed a path aggregation network consisting of a PixelShuffle-based (Shuffle-Panet) and an effective pyramidal convolutional block attention module (EPA-CBAM) to improve the detection performance of small and occluded pedestrian targets.…”

Section: Improved Pedestrian Detection Methods Based On Deep Learningmentioning

confidence: 99%

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Zhan

Zhu

et al. 2023

Entropy

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Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network’s adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.

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Section: Improved Pedestrian Detection Methods Based On Deep Learningmentioning

confidence: 99%

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Zhan

Zhu

et al. 2023

Entropy

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“…Second, the upsampling magnification is set to 2. Assuming that the upsampling core size is k r × k r , the feature map is reorganized into 2H × 2W × k r × k r through pixel shuffling [33]. Last, normalization is performed by softmax.…”

Section: Lightweight Upsampling Operator Carafementioning

confidence: 99%

“…Horticulturae 2024, 10, x FOR PEER REVIEW 6 of 23 is kr × kr, the feature map is reorganized into 2H × 2W × kr × kr through pixel shuffling [33].…”

Section: Lightweight Upsampling Operator Carafementioning

confidence: 99%

Study on Target Detection Method of Walnuts during Oil Conversion Period

Fu,

Wang,

Zhang

et al. 2024

Horticulturae

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The colors of walnut fruits and leaves are similar in the oil transformation period, and the fruits are easily blocked by the branches and leaves. On the basis of the improved YOLOv7-tiny, a detection model is proposed and integrated into an Android application to solve the problem of walnut identification. Ablation experiments conducted with three improved strategies show that the strategies can effectively enhance the performance of the model. In terms of combinatorial optimization, the YOLOv7-tiny detection model that combines FasterNet and LightMLP modules works excellently. Its AP50 and AP50–95 are 3.1 and 4 percentage points (97.4% and 77.3%, respectively) higher than those of the original model. YOLOv7-tiny’s model size and number of parameters are reduced by 14.6% and 14.4%, respectively, relative to those of the original model, and its detection time decreases to 15.4 ms. The model has good robustness and generalization ability and can provide a technical reference for intelligent real-time detection of walnuts during the oil conversion period.

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“…To address the issue of compromised feature information for small-scale and occluded targets during the up-sampling process in YOLOv4, Wang et al proposed the SE-YOLOv4 [15] pedestrian detection model. They introduced the Shuffle-PANet, which replaces the YOLOv4 backbone network.…”

Section: Related Workmentioning

confidence: 99%

A Pedestrian Detection Algorithm Based on Improved YOLOv7-tiny

2024

JSSH

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With the widespread development of embedded device applications based on deep learning, pedestrian detection using deep learning often requires the use of complex and large neural networks. However, these networks have a large memory footprint, which makes them difficult to deploy on embedded devices, and are computationally intensive, which makes it difficult to guarantee the speed of operation on embedded devices. To address these issues, a lightweight network, YOLO-Micro, is proposed in this paper. The model reduces the complexity of the model by replacing the original module in YOLOv7-tiny with the G-ELAN module proposed in this paper. Meanwhile, the attention mechanism and the activation function were introduced and replaced to enhance the feature extraction capability of the model. Finally, the model was successfully deployed to Android mobile. Experimental results on the PASCAL VOC Pedestrian dataset show that the inference speed of YOLO-Micro is 20.8% faster than the original model, and the mAP is improved from 77.95% to 79.42% of the original model.

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SE-YOLOv4: shuffle expansion YOLOv4 for pedestrian detection based on PixelShuffle

Cited by 10 publications

References 44 publications

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Study on Target Detection Method of Walnuts during Oil Conversion Period

A Pedestrian Detection Algorithm Based on Improved YOLOv7-tiny

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