Spatio-Temporal Feature Encoding for Traffic Accident Detection in VANET Environment

Zhou, Zhili; Dong, Xiaohua; Li, Zhetao; Yu, Keping; Ding, Chun; Yang, Yimin

doi:10.1109/tits.2022.3147826

Cited by 95 publications

(44 citation statements)

References 42 publications

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“…HE-Yolo is compared with other methods [ 3 , 7 , 12 ], and the mAP value, the training convergence epoch, and the single-frame recognition time are tested, respectively. The comparison results are listed in Table 6 .…”

Section: Results and Analysismentioning

confidence: 99%

“…The mAP value of HE-Yolo is 4.04%, 5.04% and 1.76% higher than that of Yolo v3, SSD, and faster R–CNN, and the mAP value of HE-Yolo is also higher than that of other models under different lighting conditions. Compared with other methods [ 3 , 5 , 7 ], HE-Yolo has high recognition accuracy and fast recognition speed under the condition of low hardware.…”

Section: Discussionmentioning

confidence: 99%

“…It can be seen from Table 6 that the mAP value of HE-Yolo is higher than that of other methods, and it converges after 65 iteration epochs, which shows that HE-Yolo needs lower hardware and less computation time, and its recognition speed is slightly lower than that of reference [7], but it can fully meet the real-time recognition.…”

Section: Analyzing the Influence Of Lighting Condition On The Recogni...mentioning

confidence: 96%

“…It can be seen from Figure 15 that the stability of the mAP value of HE-Yolo is significantly better than that of other models, and the mAP values under 10 luminance conditions are higher than those of other models, which shows that HE-Yolo has stronger generalization ability and better robustness to the luminance of light, so it can meet the real-time recognition requirement of day and night alternation during the breeding process. HE-Yolo is compared with other methods [3,7,12], and the mAP value, the training convergence epoch, and the single-frame recognition time are tested, respectively. e comparison results are listed in Table 6.…”

Section: Analyzing the Influence Of Lighting Condition On The Recogni...mentioning

confidence: 99%

“…In pig-posture behavior recognition, Gao et al [6] used a 3D convolutional network to recognize the aggressive pig behaviors, but some interference factors such as pig adhesion, different lightings, and complex behaviors resulted in a weak generalization ability of CNN. Zhou et al [7] designed a pig face detection method with an attention mechanism [8][9][10] combined with a feature pyramid structure, which made the network more focused on the effective information area of the images, but the gradient dispersion appeared with the deepening of the number of the network layers. Yang et al [11] used CNN to recognize the individual pig-drinking behaviors, but the method showed the complex calculation of feature selection and relied on high hardware.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Huang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision P, the recall R, the AP (i.e., the area of P-R curve) and the MAP (i.e., the average value of AP). The experiment results show that the AP values of HE-Yolo reach 99.25%, 98.41%, 94.43%, and 97.63%, respectively, in the recognition of four pig-posture behaviors of standing, sitting, prone and sidling of the test set. Compared with other models such as Yolo v3, SSD, and faster R–CNN, the mAP value of HE-Yolo is increased by 5.61%, 4.65%, and 0.57%, respectively, and the single-frame recognition time of HE-Yolo is only 0.045 s. In the recognition of images with foreign body occlusion and pig adhesion, the mAP values of HE-Yolo are increased by 4.04%, 4.94%, and 1.76%, respectively, while compared with other models. Under different lighting conditions, the mAP value of HE-Yolo is also higher than that of other models. The experimental results show that HE-Yolo can recognize the pig-posture behaviors with high precision, and it shows good generalization ability and luminance robustness, which provides technical support for the recognition of pig-posture behaviors and real-time monitoring of physiological health of the enclosure pigs.

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Section: Results and Analysismentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Analyzing the Influence Of Lighting Condition On The Recogni...mentioning

confidence: 96%

Section: Analyzing the Influence Of Lighting Condition On The Recogni...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Huang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Energy‐efficient computation offloading and resource allocation in delay‐constrained vehicular edge network

Yang

Lin

Saini

et al. 2022

Int J Communication

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SummaryCellular vehicular‐to‐everything (C‐V2X), as a key technology of Internet of Vehicles (IoV), is promised to be enhanced and strengthened to improve road traffic safety and achieve intelligent transportation in 6G era. However, computation‐intensive and latency‐sensitive computation tasks of autonomous driving create a great challenge for the computation and storage limited vehicles. Fortunately, mobile edge computing (MEC) architecture offers a possible solution for the challenge. In this paper, a joint computation offloading and resource allocation algorithm is proposed to solve the computation tasks offloading problem in the scenario of vehicular edge computing network. First, the computation offloading and resource allocation are jointly modeled as a mixed integer nonlinear optimization problem. Aiming at minimizing the total system cost (weighted sum of delay and energy consumption), a particle encoding method and a particle refinement algorithm are proposed based on the compression factor particle swarm optimization, and multilevel penalty function is adopted to deal with the constraints of the objective. Finally, experimental performances validate the effectiveness and feasibility of the proposed algorithm.

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A CNN-Based Road Accident Detection and Comparison of Classification Techniques

Gosal,

Hota,

Kumar

2024

Lecture Notes in Networks and Systems

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Spatio-Temporal Feature Encoding for Traffic Accident Detection in VANET Environment

Cited by 95 publications

References 42 publications

Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism

Energy‐efficient computation offloading and resource allocation in delay‐constrained vehicular edge network

A CNN-Based Road Accident Detection and Comparison of Classification Techniques

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