YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities

Song, Fangzheng; Li, Peng

doi:10.3390/biomimetics8060480

Cited by 9 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, while synthetic fog enhancement algorithms can adapt to foggy conditions, their performance assurance in other adverse weather conditions remains unclear. Song et al [45] improved the inference speed and accuracy of the YOLOv5-MS network for pedestrian detection through video stream multi-threading capture and module optimization. Yet, multi-threading capture and module optimization might increase the system's overall complexity, posing a challenge in balancing resource usage and algorithm performance in computationally limited environments.…”

Section: Research On Lightweight Strategies For Object Detection Modelsmentioning

confidence: 99%

CMCA-YOLO: A Study on a Real-Time Object Detection Model for Parking Lot Surveillance Imagery

Zhao,

Wang,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

In the accelerated phase of urbanization, intelligent surveillance systems play an increasingly pivotal role in enhancing urban management efficiency, particularly in the realm of parking lot administration. The precise identification of small and overlapping targets within parking areas is of paramount importance for augmenting parking efficiency and ensuring the safety of vehicles and pedestrians. To address this challenge, this paper delves into and amalgamates cross-attention and multi-spectral channel attention mechanisms, innovatively designing the Criss-cross and Multi-spectral Channel Attention (CMCA) module and subsequently refining the CMCA-YOLO model, specifically optimized for parking lot surveillance scenarios. Through meticulous analysis of pixel-level contextual information and frequency characteristics, the CMCA-YOLO model achieves significant advancements in accuracy and speed for detecting small and overlapping targets, exhibiting exceptional performance in complex environments. Furthermore, the study validates the research on a proprietary dataset of parking lot scenes comprising 4502 images, where the CMCA-YOLO model achieves an mAP@0.5 score of 0.895, with a pedestrian detection accuracy that surpasses the baseline model by 5%. Comparative experiments and ablation studies with existing technologies thoroughly demonstrate the CMCA-YOLO model’s superiority and advantages in handling complex surveillance scenarios.

show abstract

Section: Research On Lightweight Strategies For Object Detection Modelsmentioning

confidence: 99%

CMCA-YOLO: A Study on a Real-Time Object Detection Model for Parking Lot Surveillance Imagery

Zhao,

Wang,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Inspired by the biological nervous system, deep neural networks (DNNs) have been successfully utilized in various tasks [1][2][3][4][5][6], particularly in computer vision [7][8][9][10][11][12]. This is made possible by large-scale datasets with accurate labels, although collecting them can be challenging and costly, especially in certain professional fields that require personnel with relevant professional knowledge to label samples.…”

Section: Introductionmentioning

confidence: 99%

CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency

Dong,

Li,

Wang

et al. 2024

Biomimetics

View full text Add to dashboard Cite

Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. These methods cross-train two DNNs based on the small-loss criterion and employ a strategy using either “disagreement” or “consistency” to obtain the divergence of the two networks. However, these methods are sample-inefficient for generalization in noisy scenarios. In this paper, we propose CoDC, a novel Co-teaching-basedmethod for accurate learning with label noise via both Disagreement and Consistency strategies. Specifically, CoDC maintains disagreement at the feature level and consistency at the prediction level using a balanced loss function. Additionally, a weighted cross-entropy loss is proposed based on information derived from the historical training process. Moreover, the valuable knowledge involved in “large-loss” samples is further developed and utilized by assigning pseudo-labels. Comprehensive experiments were conducted on both synthetic and real-world noise and under various noise types. CoDC achieved 72.81% accuracy on the Clothing1M dataset and 76.96% (Top1) accuracy on the WebVision1.0 dataset. These superior results demonstrate the effectiveness and robustness of learning with noisy labels.

show abstract

“…Human action recognition technology has been widely used in the fields of bionic robots, medical care, video surveillance, intelligent transportation, and human–robot interaction [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Skeletons are made up of a variety of different shapes, with complex internal and external structures, which play a role in supporting and protecting the human body.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Enhancement Transformer Network for Skeleton-Based Human Interaction Recognition

Hu,

Liu

2024

Biomimetics

View full text Add to dashboard Cite

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods.

show abstract

YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities

Cited by 9 publications

References 36 publications

CMCA-YOLO: A Study on a Real-Time Object Detection Model for Parking Lot Surveillance Imagery

CMCA-YOLO: A Study on a Real-Time Object Detection Model for Parking Lot Surveillance Imagery

CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency

Multi-Modal Enhancement Transformer Network for Skeleton-Based Human Interaction Recognition

Contact Info

Product

Resources

About