Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Shao, Xiaotao; Wang, Qing; Yang, Wei; Chen, Yun; Xie, Yi; Shen, Yan; Wang, Zhongli

doi:10.3390/s21051820

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…Zhang [11] designed the OR-CNN network and utilized the PORol pooling unit to partition the human body into five parts for feature fusion. Shao [12] inspired by FPN, proposed the multi-scale feature pyramid network (MFPN) based on ResNet [13] . It fuses features from different scales to enhance information extraction ability for occluded targets.…”

Section: Introductionmentioning

confidence: 99%

A network model based on scrap metal classification and grading

Yu,

Xu,

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Due to the complex scenes of reality in the scrapyard, the overlapping between scrap metals causes occlusion, resulting in missed detection and false detection problems during the classification and grading of scrap metals. In response to this problem, we propose a network model for scrap metal classification and grading, named FG-Net, which improves Yolov5s. First, we replace the convolutional layers in the backbone with the proposed MBCC module, so that the backbone network can adopt strategies such as depth-wise separable convolution, residual connection, channel and spatial expansion, which not only improves the feature extraction ability, but also enhances the efficiency and accuracy of the network. In the feature fusion stage of this model, we introduce the RFB module to improve the receptive field and increase the flexibility of feature fusion, enabling the model to fuse more feature information. Finally, an improved loss function is used to address the missed detection and false detection issues caused by the closeness of scrap metals.This paper first verifies the model on the official datasets PASCAL VOC(2007+2012) and COCO, with mAPs of 84.6% and 59.8%, respectively, which are 2.1% and 3% higher than Yolov5s. The accuracy of the self-made scrap metal dataset reaches 87.4%, with an mAP of 86.1%, which is 10.1% and 10.4% higher than Yolov5s, respectively. Through experiments, it is proved that this model not only improves the accuracy of scrap metal recognition, but also solves the occlusion problem in the current scrap metal classification and grading process.

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Section: Introductionmentioning

confidence: 99%

A network model based on scrap metal classification and grading

Yu,

Xu,

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…This approach overcomes the limitation of single proposal boxes predicting a single target and introduces the CrowdDet network model, capable of predicting multiple targets from a single proposal box. Building on this, Shao et al [11] used a residual network (ResNet) as a base and enhanced the detection rate for targets with incomplete feature information by integrating multi-scale feature pyramids for feature fusion. To further improve the detection of occluded objects, Yang et al [12] proposed a combined Rep-GIoU loss by blending repulsion loss with GIoU loss.…”

Section: Introductionmentioning

confidence: 99%

Complex Scene Occluded Object Detection with Fusion of Mixed Local Channel Attention and Multi-Detection Layer Anchor-Free Optimization

Su,

2024

Automation

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The field of object detection has widespread applicability in many areas. Despite the multitude of object detection methods that are already established, complex scenes with occlusions still prove challenging due to the loss of information and dynamic changes that reduce the distinguishable features between the target and its background, resulting in lower detection accuracy. Addressing the shortcomings in detecting obscured objects in complex scenes with existing models, a novel approach has been proposed on the YOLOv8n architecture. First, the enhancement begins with the addition of a small object detection head atop the YOLOv8n architecture to keenly detect and pinpoint small objects. Then, a blended mixed local channel attention mechanism is integrated within YOLOv8n, which leverages the visible segment features of the target to refine the feature extraction hampered by occlusion impacts. Subsequently, Soft-NMS is introduced to optimize the candidate bounding boxes, solving the issue of missed detection under overlapping similar targets. Lastly, using universal object detection evaluation metrics, a series of ablation experiments on public datasets (CityPersons) were conducted alongside comparison trials with other models, followed by testing on various datasets. The results showed an average precision (map@0.5) reaching 0.676, marking a 6.7% improvement over the official YOLOv8 under identical experimental conditions, a 7.9% increase compared to Gold-YOLO, and a 7.1% rise over RTDETR, also demonstrating commendable performance across other datasets. Although the computational load increased with the addition of detection layers, the frames per second (FPS) still reached 192, which meets the real-time requirements for the vast majority of scenarios. Such findings indicate that the refined method not only significantly enhances performance on occluded datasets but can also be transferred to other models to boost their performance capabilities.

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“…Shao et al [12] combined FPN and ResNet to extract and fuse image features, which enhanced the semantic information and contour of occluded pedestrians and improved the performance of pedestrian detection. Pereira et al [13] compared the impact of different data-association measures on the tracking effect of the DeepSORT algorithm; it can be seen that better data-association measures can improve the tracking effect of tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved DeepSORT Algorithm Based on Multi-Feature Fusion

Liu

Pei

Bei

et al. 2022

ASI

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At present, the detection-based pedestrian multi-target tracking algorithm is widely used in artificial intelligence, unmanned driving cars, virtual reality and other fields, and has achieved good tracking results. The traditional DeepSORT algorithm mainly tracks multiple pedestrian targets continuously, and can keep the ID unchanged. The applicability and tracking accuracy of the algorithm need to be further improved during tracking. In order to improve the tracking accuracy of the DeepSORT method, we propose a novel algorithm by revising the IOU distance metric in the matching process and integrating Feature Pyramid Network (FPN) and multi-layer pedestrian appearance features. The improved algorithm is verified on the public MOT-16 dataset, and the tracking accuracy of the algorithm is improved by 4.1%.

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Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Cited by 11 publications

References 39 publications

A network model based on scrap metal classification and grading

A network model based on scrap metal classification and grading

Complex Scene Occluded Object Detection with Fusion of Mixed Local Channel Attention and Multi-Detection Layer Anchor-Free Optimization

Improved DeepSORT Algorithm Based on Multi-Feature Fusion

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