Anchor-free infrared pedestrian detection based on cross-scale feature fusion and hierarchical attention mechanism

Hao, Shuai; Gao, Shan; Xu, Ma; An, Beiyi; Tian, He

doi:10.1016/j.infrared.2023.104660

Cited by 10 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of sensor and communication technologies, fault detection based on the dynamic response of wheels and rails has been widely used. Rail inspection has shifted to the use of sensors and automated equipment such as ultrasonic detection and eddy current detection [ 3 , 4 , 5 , 6 , 7 ]. This method uses sensors to capture vibration and acceleration signals during operation and analyzes these signals to identify abnormalities in the wheel-rail system and determine faults [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Du,

Zhang,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

show abstract

Section: Introductionmentioning

confidence: 99%

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Du,

Zhang,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…At present, many scholars at home and abroad have conducted much research on underground coal mine target detection using machine vision technology and have achieved more remarkable results and progress [5][6][7][8]. Liang et al [9] proposed a drilling robot pressure relief hole-identification method based on a single image generation adversarial network (SinGAN).…”

Section: Introductionmentioning

confidence: 99%

Research on the Detection Method of Coal Mine Roadway Bolt Mesh Based on Improved YOLOv7

Sun

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.

show abstract

“…Liu et al [19] deepened the spatial pyramidal pooling structure of the YOLOv4 network and introduced weight coefficients to the balanced cross entropy to increase the loss function's contribution. Hao et al [20] incorporated an attention mechanism into the YOLOv5 network to detect faults in transmission lines while retaining the network's fast detection capability. Zhang et al [21] improved the feature extraction network of YOLOv7 by incorporating the CBAM attention mechanism and a centralized pyramid structure in the deeper layers.…”

mentioning

confidence: 99%