Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model.

/pdf/asff-yolov5-multielement-detection-method-for-road-traffic-1r2p7mr2.pdf

ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion

In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is utilized to improve loss function for promoting the positioning accuracy of the small object. In order to reduce the complexity of the model, we present a lightweight yet powerful backbone network (named SA-MobileNeXt) that incorporates channel and spatial attention. Our approach can extract expressive features more effectively by applying the Shuffle Channel and Spatial Attention (SCSA) module into the SandGlass Block (SGBlock) module while increasing the parameters by a small number. In addition, the data enhancement method combining Mosaic and Mixup is employed to improve the robustness of the training model. The Multi-scale Feature Enhancement Fusion (MFEF) network is proposed to fuse the extracted features better. In addition, the SiLU activation function is utilized to optimize the Convolution-Batchnorm-Leaky ReLU (CBL) module and the SGBlock module to accelerate the convergence of the model. The ablation experiments on the KITTI dataset show that each improved method is effective. The improved algorithm reduces the complexity and detection speed of the model while improving the object detection accuracy. The comparative experiments on the KITTY dataset and CCTSDB dataset with other algorithms show that our algorithm also has certain advantages.

Small Object Detection in Traffic Scenes Based on YOLO-MXANet.

Defect detection is crucial in steel production to prevent safety risks and prolong lifespan. However, current methods still face challenges in accurately detecting small and vague targets. In response to this problem, we propose a model named SRN-YOLO for steel surface detection based on YOLOv7. Firstly, a split residual convolution network (SResNet) is designed to capture gradient feature information. Then, a re-fusion feature pyramid network (RFPN) is built to minimize the loss of features. Furthermore, a location regression loss function named NWD-CIOU combined the Normalized Wasserstein Distance (NWD) and Complete Intersection Over Union (CIOU) is employed to increase the number of positive samples of small targets. The experiment results show that SRN-YOLO has excellent detection performance for steel surface defects. Specifically, the recognition accuracies of the model on NEU-DET and GC10-DET reach 81.2 mAP and 71.6 mAP, respectively, which are improved by 5.8% and 3.8% compared to YOLOv7. 

A detection network for small defects of steel surface based on YOLOv7

The high altitude images captured by drones often contain small-sized objects, which pose several challenges including significant scale variations and dense clustering. To tackle these challenges in small object detection, this paper presents EM-YOLO (Efficient Switching Network and Multi-Scale Feature Fusion Reinforced YOLO for Small Object Detection), an enhanced algorithm based on the YOLOv5s architecture. Firstly, to enhance the network's adaptability to varying object feature scales, the EM-YOLO redesigns the feature extraction backbone network by using different dilation rates in convolutions within the backbone. This flexible structure allows for improved extraction of object features, enhancing the network's feature extraction capability. Secondly, additional detection layer for small objects and combines it with the dynamic detection head. This combination addresses the issue of small object information loss due to multiple down-sampling operations on the feature maps. Finally, Focal-EIoU Loss is employed to enhance regression accuracy in EM-YOLO. Extensive experiments were conducted on the VisDrone2019-DET dataset. The results of these experiments demonstrate that the improved algorithm achieved a significant increase in performance. Specifically, the algorithm achieves 43.5% and 25.1% on the mAP0.5 and mAP0.5:0.95 metrics, which are 13.9% and 8.9% higher than YOLOv5s algorithm, respectively. Furthermore, comparative experiments with other mainstream algorithms indicate that EM-YOLO outperforms them in the task of small object detection. 

Detection algorithm for dense small objects in high altitude image

Daily road maintenance is essential to road safety and serviceability, particularly to highways. In daily road maintenance inspection tasks, the objectives include a variety of targets, such as pavement cracks, foreign objects, guardrail damages etc. There is a lack of rapid detection methods that allow for the uniform identification of multiple targets for road maintenance. This paper proposes an automatic multi-object detection method for road daily maintenance inspection assisted by unmanned aerial vehicles (UAV). A dataset of multiple roadway anomalies (UAVROAD) for daily road maintenance needs was constructed. UM-YOLO, an improved algorithm based on the YOLOv8n algorithm was created to better extract the features of multiple targets, as well as fuse features and reduce computation while maintaining accuracy. The improvements include adding EMA (Efficient multiscale Attention Module) in the C2f module in the backbone, employing Bi-FPN fusion mechanism in the neck and using GSConv, a lightweight convolutional network, for the convolution operation. Compared with the YOLOv8n, the proposed UM-YOLO improved mean average precision(mAP) by 4.6% and reduced the model computation by 14%.

Multi-Object Detection for Daily Road Maintenance Inspection With UAV Based on Improved YOLOv8

Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.

https://www.mdpi.com/1424-8220/21/15/5194/pdf

Centered Multi-Task Generative Adversarial Network for Small Object Detection.

The invention provides an integrated micro-miniature universal pan-tilt which can realize pitch transmission precision, structure miniaturization, compactness, the light weight, installation manner generalization and high work integration. The universal pan-tilt includes a pitch mechanism, a bracket assembly, a support assembly, an electrical component and a shield. According to pitch transmission, a micro servo electric push rod is used for driving, a ball screw pair is used inside the electric push rod to conduct transmission, precise pitch transmission can be achieved, telescopic movement of the ball screw pair can be performed, the high and low motion pitch can be achieved, meanwhile, rigidity is good, the self-locking function is achieved, the unbalanced torque can be balanced when motion is stopped at any position, and the force balance is maintained. Meanwhile, a common installation interface for various task loads is reserved in the upper portion of the platform, and installing ports allowing quick installation on various unmanned platforms to be achieved are reserved in the lower portion.

Integrated micro-miniature universal pan-tilt

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Centered Multi-Task Generative Adversarial Network for Small Object Detection.

Integrated micro-miniature universal pan-tilt