Stacked Multi-Scale Attention Network for Image Colorization

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

Section: Methodsmentioning

confidence: 99%

Lightweight Vehicle Detection Based on Improved YOLOv5s

Wang,

Xu,

Wang

et al. 2024

“…This advancement is primarily exemplified by the Single Shot MultiBox Detector (SSD) [ 11 ], the YOLO series [ 12 , 13 , 14 , 15 , 16 ], and RetinaNet [ 17 ]. Additionally, the introduction of feature pyramid networks (FPN) [ 18 ], attention mechanisms [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ], and other structures have further enhanced the detection performance of single-stage object detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…This process reduces reliance on external information and enhances internal correlation. The Efficient Multi-Scale Attention Module (EMA) [ 24 ] algorithm combines the feature information from two parallel branches by emphasizing dimension interaction. It effectively captures the pairwise relationship between pixels and enhances the pixel-level attention for more advanced feature information.…”

Section: Related Workmentioning

confidence: 99%

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection

Tang,

Yun,

Chen

et al. 2024

Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion process, significantly compromising the network’s detection performance on traffic targets. This paper presents a novel methodology by which to overcome the concerns above, namely HRYNet. Firstly, a dual fusion gradual pyramid structure (DFGPN) is introduced, which employs a two-stage gradient fusion strategy to enhance the generation of more comprehensive multi-scale high-level semantic information, strengthen the interconnection between non-adjacent feature layers, and reduce the information gap that exists between them. HRYNet introduces an anti-interference feature extraction module, the residual multi-head self-attention mechanism (RMA). RMA enhances the target information by implementing a characteristic channel weighting policy, thereby reducing background interference and improving the attention capability of the network. Finally, the detection performance of HRYNet was evaluated by utilizing three datasets: the horizontally collected dataset BDD1000K, the UAV high-altitude dataset Visdrone, and a custom dataset. Experimental results demonstrate that HRYNet achieves a higher mAP_0.5 compared with YOLOv8s on the three datasets, with increases of 10.8%, 16.7%, and 5.5%, respectively. To optimize HRYNet for mobile devices, this study presents Lightweight HRYNet (LHRYNet), which effectively reduces the number of model parameters by 2 million. The results demonstrate that LHRYNet outperforms YOLOv8s in terms of mAP_0.5, with improvements of 6.7%, 10.9%, and 2.5% observed on the three datasets, respectively.

“…Due to the complex and variable background of the track surface and the difficulty in detecting subtle defects, the EMA attention mechanism is introduced in order to further enhance the screening and filtering abilities of the network on key information and improve the performance of the algorithm. EMA is a cross-space learning approach proposed by Daliang Ouyang et al, Efficient Multi-Scale Attention, that can interact with information without channel dimensionality reduction and reduce computational overhead [ 41 ]. Its structure is shown in Figure 7 .…”

Section: Rsdnet: Yolov8n-cdconv-bifpn-emamentioning

confidence: 99%

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Du,

Zhang,

Gao

et al. 2024

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.