Nowadays, vehicles with a high level of automation are being driven everywhere. With the apparent success of autonomous driving technology, we keep working to achieve fully autonomous vehicles on roads. Efficient and accurate vehicle detection is one of the essential tasks in the environment perception of an autonomous vehicle. Therefore, numerous algorithms for vehicle detection have been developed. However, their strengths in terms of performance have not been deeply assessed or highlighted yet. This work comprehensively reviews the existing methods and datasets for vehicle detection considering their performances and applications in the field of autonomous driving. First, we briefly describe tasks, evaluation criteria, and existing public datasets for vehicle detection in autonomous driving. Second, we provide a rigorous review of both classical and latest vehicle detection methods, including machine vision-based, mmWave radar-based, LiDAR-based, and sensor fusion-based methods. Finally, we analyze the pertinent challenges of autonomous vehicles and provide recommendations for future works concerning vehicle detection. The present review covers over 300 research works and aims to help researchers interested in autonomous driving, especially in vehicle detection. 

Vehicle Detection for Autonomous Driving: A Review of Algorithms and Datasets

The distribution of transmission lines is complex, influenced by terrain and topography, making their maintenance and management challenging. With the advancement of global mechanization, various types of construction machinery and large‐scale mechanical equipment are continually increasing. Accidental contact of construction machinery near transmission lines poses a serious threat to the stability of the power system. Traditional drones and inspection robots face difficulties in achieving real‐time monitoring of construction machinery and equipment around transmission lines, quickly identifying potential risks. Conventional image processing techniques and convolutional neural networks struggle with effective handling of small targets and densely packed detection tasks involving multiple targets. To address these challenges, this paper proposes an intelligent detection algorithm Swin Transformer attention efficient algorithm‐you only look once (YOLO) (STAE‐YOLO). It is based on the Swin Transformer global self‐attention mechanism, cross‐channel fusion attention mechanism, enhanced small object detection framework, and a focused and efficient regression loss function. The experimental results show that the STAE‐YOLO algorithm model improved by 6.3% in mean average precision, 3.7% in precision, and 3.1% in recall compared to the baseline model. Meanwhile, deploying the window multi‐head self‐attention global self‐attention mechanism in the model can strengthen the global multi‐scale semantic information in detection.

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere with the proposed CatBoost with the MLP model. The proposed model aims to enhance the interpretability of predictions in breast cancer diagnosis by leveraging the benefits of CatBoost classification technique in feature identification and also contributing towards the interpretability of the decision model. The proposed CatBoost+MLP has been evaluated using the Shapley additive explanations values to analyze the feature significance in decision-making. Initially, the feature engineering is done using the analysis of variance technique to identify the significant features. The MLP model alone and the CatBoost+MLP model are being analyzed using divergent performance metrics, and the results obtained are compared with contemporary breast cancer identification techniques. 

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer

Feature matching of monocular laparoscopic videos is crucial for visualization enhancement in computer-assisted surgery, and the keys to conducting high-quality matches are accurate homography estimation, relative pose estimation, as well as sufficient matches and fast calculation. However, limited by various monocular laparoscopic imaging characteristics such as highlight noises, motion blur, texture interference and illumination variation, most exiting feature matching methods face the challenges of producing high-quality matches efficiently and sufficiently. To overcome these limitations, this paper presents a novel sequential coupling feature descriptor to extract and express multilevel feature maps efficiently, and a dual-correlate optimized coarse-fine strategy to establish dense matches in coarse level and adjust pixel-wise matches in fine level. Firstly, a novel sequential coupling swin transformer layer is designed in feature descriptor to learn and extract multilevel feature representations richly without increasing complexity. Then, a dual-correlate optimized coarse-fine strategy is proposed to match coarse feature sequences under low resolution, and the correlated fine feature sequences is optimized to refine pixel-wise matches based on coarse matching priors. Finally, the sequential coupling feature descriptor and dual-correlate optimization are merged into the Sequential Coupling Dual-Correlate Network (SeCo DC-Net) to produce high-quality matches. The evaluation is conducted on two public laparoscopic datasets: Scared and EndoSLAM, and the experimental results show the proposed network outperforms state-of-the-art methods in homography estimation, relative pose estimation, reprojection error, matching pairs number and inference runtime. The source code is publicly available at https://github.com/Iheckzza/FeatureMatching. 

Dual-correlate optimized coarse-fine strategy for monocular laparoscopic videos feature matching via multilevel sequential coupling feature descriptor

Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN. Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well.

/pdf/instance-segmentation-of-irregular-deformable-objects-for-1zb5bitu.pdf

Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module

Object detection technology plays a crucial role in people's everyday lives, as well as enterprise production and modern national defense. Most current object detection networks, such as YOLOX, employ convolutional neural networks instead of a Transformer as a backbone. However, these techniques lack a global understanding of the images and may lose meaningful information, such as the precise location of the most active feature detector. Recently, a Transformer with larger receptive fields showed superior performance to corresponding convolutional neural networks in computer vision tasks. The Transformer splits the image into patches and subsequently feeds them to the Transformer in a sequence structure similar to word embeddings. This makes it capable of global modeling of entire images and implies global understanding of images. However, simply using a Transformer with a larger receptive field raises several concerns. For example, self-attention in the Swin Transformer backbone will limit its ability to model long range relations, resulting in poor feature extraction results and low convergence speed during training. To address the above problems, first, we propose an important region-based Reconstructed Deformable Self-Attention that shifts attention to important regions for efficient global modeling. Second, based on the Reconstructed Deformable Self-Attention, we propose the Swin Deformable Transformer backbone, which improves the feature extraction ability and convergence speed. Finally, based on the Swin Deformable Transformer backbone, we propose a novel object detection network, namely, Swin Deformable Transformer-BiPAFPN-YOLOX. experimental results on the COCO dataset show that the training period is reduced by 55.4%, average precision is increased by 2.4%, average precision of small objects is increased by 3.7%, and inference speed is increased by 35%.

/pdf/object-detection-based-on-swin-deformable-transformer-3tl6sffl.pdf

Object Detection Based on Swin Deformable Transformer-BiPAFPN-YOLOX

MT-Net: Fast video instance lane detection based on space time memory and template matching

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Object Detection Based on Swin Deformable Transformer-BiPAFPN-YOLOX

MT-Net: Fast video instance lane detection based on space time memory and template matching