Preceding Vehicle Detection Method Based on Visual Fusion

Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved.

The fabric defect detection algorithm based on object detection has become a research hotspot. The method based on the Single Shot MultiBox Detector (SSD) model has a fast detection speed, but the detection accuracy is insufficient. To balance the detection speed and accuracy of the model and meet the actual needs of the industry, an improved fabric defect detection algorithm based on SSD is proposed in this study. The Fully Convolutional Squeeze-and-Excitation (FCSE) block is added into the traditional SSD to improve the detection accuracy of the model. The number of default boxes was adjusted to accommodate the detection of long strip defects on fabric surface. Experimental results on the TILDA and Xuelang dataset confirm that our detection method based on SSD efficiently detected various fabric defects.

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.

Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.

With the ever-increasing demand for marine products with high nutritional value in the sea, rapid and accurate detection of target organisms is highly desired. In this context, a novel underwater object detection algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed to improve the detection accuracy and real-time performance in complex underwater environment. The proposed algorithm employs ResNeXt-50 instead of VGGNet-16 as the backbone network to improve the ability to obtain details and position information of the target object. To be more specific, a channel-spatial attention mechanism is incorporated in the deep layers of the improved XC-SSD model to enhance semantic information of high-level feature maps and reduce missed detection rate and false detection rate. Comprehensive experiments on the URPC-2019 dataset show that the detection accuracy and speed of the proposed algorithm are 79.76% and 18.95 FPS, respectively, which are 3.49% and 2.8 FPS higher than the original SSD algorithm. Experimental results clearly demonstrate that the proposed algorithm is highly efficient and suitable for real-time object detection.

Despite the extensive investigation of binocular and stereoscopic vision, relatively little is known about its importance in natural visually guided behavior. In this paper, we explored the role of binocular vision when walking over and around obstacles. We monitored eye position during the task as an indicator of the difference between monocular and binocular performances. We found that binocular vision clearly facilitates walking performance. Walkers were slowed by about 10% in monocular vision and raised their foot higher when stepping over obstacles. Although the location and sequence of the fixations did not change in monocular vision, the timing of the fixations relative to the actions was different. Subjects spent proportionately more time fixating the obstacles and fixated longer while guiding foot placement near an obstacle. The data are consistent with greater uncertainty in monocular vision, leading to a greater reliance on feedback in the control of the movements.

critical fusion frequency (CFF), or the flicker fusion threshold, is defined as the critical frequency above which a flickering light source appears steady to the observer (see [Fig. 1][1]). It is known to depend on a number of parameters, namely, intensity of the light source ([5][2], [15][3]),

Increasing driving safety by virtue of advanced technology requires real-time and accurate detection of vehicles in far and close distance, which pose a threat to the host vehicle. This paper presents a real-time on-road vehicle detection algorithm based on monocular vision. First, auto-adapted threshold segmentation is proposed to extract shadow features. Then, a special mask is used for morphology computing to retain the features of vehicles in far and close distance. And, vanishing point constraint is applied for the fast verification of vehicles. Finally, the tracking of vehicles assists to stabilize the detection results. The experiments show that the average processing speed reaches 39 frames/ms, and the fast detection of vehicle under different weather conditions in the day time can also work accurately.

Vehicle detection is an important technology in au-tonomous driving, for which high detection accuracy and real-time performance are often required. The YOLOv5-GE vehicle detection algorithm is proposed to address the situation that the YOLOv5 vehicle detection model has false detection and missed detection for small and dense targets in complex environments. The global attention mechanism is added to the backbone net-work of the YOLOOv5 model, which is composed of two inde-pendent submodules of channel attention and convolutional spa-tial attention, which prevents the loss of information to a certain extent and amplifies the interaction of global dimensions. Second-ly, the training process is optimized using the Focal-EloU loss function to replace the GloU loss function, which improves the accuracy of vehicle detection. Finally, the proposed YOLOv5-GE algorithm and the YOLOv5 algorithm are subjected to a con-trolled experiment on the KITTI dataset. The experimental re-sults show that the YOLOv5-GE algorithm achieves an average accuracy of 86% while maintaining real-time performance, which is 2.5% higher than that of the YOLOv5 algorithm, and can im-prove the detection accuracy of small and dense targets in com-plex environments.

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.

An incremental Learning of infrared vehicle detection method based on SSD(Single Shot MultiBox Detector) was proposed to solve the problems such as lack of detail in infrared vehicle images, difficulty in extracting feature information and low detection accuracy. Through incremental learning and training, the CNN detection model based on SSD was fine-tuning, and the Pascal VOC data set was pre-trained. Through this detection method, the infrared vehicle could be effectively identified and located effectively. Finally, the results of infrared vehicle detection using incremental data set and non-incremental data set are compared. The experimental results show that the error detection and missing detection of infrared vehicle detection are obviously improved by using the incremental data set, and the mAP is improved by 10.61%.

Aiming at the problems of low detection and tracking accuracy, poor robustness and inaccurate vehicle flow statistics of multi-vehicle types in surveillance video, an artificial intelligence-based vehicle detection and tracking method was proposed. This method reconstructed the feature extraction network of the target detection model YOLOv5s to enhance the extraction of important features of the target and improve the detection accuracy of the detector. Firstly, Swin Transformer module is introduced into the Backbone network to replace part of C3 modules in the original algorithm, so as to enhance the global modeling ability of the model, better capture the context feature information, and expand the model's receptive field. Then, by comparing different attention mechanisms, NAM attention is selected to access the Neck network to enhance the cross-dimensional interaction between the channel and spatial dimensions of information, reduce information loss and enhance the performance of the network. Finally, the feature extraction network part of the DeepSort algorithm was optimized and retrained on the vehicle rerecognition data set to make it more suitable for vehicle tracking. The experimental results show that the accuracy of vehicle detection by the improved YOLOv5s is increased by 1.93 percentage points compared with the original algorithm. Combined with the DeepSort algorithm, the accuracy of vehicle flow statistics under different lighting conditions such as daytime, evening and night can reach 97.2%, 97.5% and 80.4% respectively.

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

Fast, robust and accurate posture detection algorithm based on Kalman filter and SSD for AGV

One of the researcher's interests and challenges is animal identification techniques. There are several challenges that researchers in this field confront that limit detection effectiveness and efficiency, such as picture illumination variation, animal occlusion, colour similarity of animal colours with backdrop environment, and so on. The purpose of this study is to detect and classify multi-label images of mammals, which we propose to do using the Single Shot Multi-Box Detector (SSD) and the MobileNet v1 coco_2017 model. Another objective is to locate and identify several items (animals) from the Mammal category in digital photos. Based on deep learning technology, the recommended SSD is regarded as a more accurate, rapid, and efficient technique to recognise objects of various sizes. We utilised 2000 pictures in the network taken from standard datasets (such as Caltech 101) and the net in this proposal. The SSD framework enhances Convolution Neural Network (CNN) detection and identification operations. During the prediction phase, the network assigns scores to the existence of each object class and draws a box around each object in the picture. Each box includes a name that defines the kind of object, and the score denotes the likelihood of the object's association to that category. During the procedure, boxes are changed to get the best fit to the form of the item. The experimental findings of this work demonstrated the efficacy of identifying and detecting animals even when light, position, and occlusion were varied. The detection and classification accuracy can reach 98.7%. Unlike other comparable efforts, this recommendation is more dependable and accurate, and it identifies a wide variety of Mammals species. Keywords— MobileNet , Convolution Neural Network (CNN), Single Shot Multi-Box Detector (SSD)