Real-Time Detection Network SI-SSD for Weak Targets in Complex Traffic Scenarios

Miao, Yongwu; Zhang, Shun; He, Shuyun

doi:10.1007/s11063-022-10762-4

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…One-stage object detection regards object detection as a regression problem and performs localization and classification at the same time. Representative algorithms include YOLO (You Only Look Once) [13], SSD [14], and RetinaNet [15].…”

Section: Related Workmentioning

confidence: 99%

Research on Fingerprint Security Based on Improved Yolo Algorithm

2022

Mobile Information Systems

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The quantitative identification technology based on the statistical law of fingerprint features has become a new research difficulty and focus, and the automatic detection and classification of fingerprint features are the basis for realizing automatic fingerprint feature statistics. In this paper, a YOLO-based fingerprint feature detection method was proposed. First, a fingerprint feature dataset was established, which contained a total of 4,000 annotated fingerprint images; then, according to the characteristics of small size and dense distribution of fingerprint feature points, the YOLO network structure was improved, the original large target feature detection layer by 32-fold downsampling was deleted, and a new small feature fusion layer was added; the FPN, PAN, and SPP structures were used to achieve local and global feature extraction through multiple-scale fusion methods; finally, the SE channel attention mechanism module was added to effectively enhance the model robustness and detection ability of dense small objects. The experimental results show that compared with the improved FP-YOLO model of the original model, when the detection speed is basically unchanged, the mAP0.5 value is increased from 93.0% to 97.4%, and the weight is reduced by 3/4.

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Section: Related Workmentioning

confidence: 99%

Research on Fingerprint Security Based on Improved Yolo Algorithm

2022

Mobile Information Systems

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“…While achieving 15-20 fps using YoloV4 and 24-30 with 72.02% mAP using YoloV4-tiny, real-time deployment on edge devices is hindered by low mAP and fps. In [23], a custom network based on SSD is proposed for detecting complex traffic scenarios, achieving an mAP of 89.05% and an overall speed of 32 fps. While meeting real-time criteria, challenges persist in deployment on embedded platforms.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Motorbike Detection: AI on the Edge Perspective

Akhtar,

Ahmed,

Yousaf

et al. 2024

Mathematics

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Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation.

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“…Heming Hu et al achieved high accuracy in strawberry detection by combining two-stage detection (Mask R-CNN) and one-stage detection (YOLOv3) networks for training and recognition [15]. To solve multiple complex situations in actual traffic scenes, Yalin Miao et al proposed a deep learning target detection network based on SI-SSD [16]. This network utilizes the feature pyramid network (FPN) and feature map fusion method to combine shallow and deep feature maps and enhance its sensitivity to small objects.…”

Section: Introductionmentioning

confidence: 99%

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

Cheng,

Chao,

Yang

et al. 2024

Applied Sciences

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Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

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Real-Time Detection Network SI-SSD for Weak Targets in Complex Traffic Scenarios

Cited by 5 publications

References 15 publications

Research on Fingerprint Security Based on Improved Yolo Algorithm

Research on Fingerprint Security Based on Improved Yolo Algorithm

Real-Time Motorbike Detection: AI on the Edge Perspective

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

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