An Intelligent Traffic Analysis and Prediction System Using Deep Learning Technique

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)

Single Shot MultiBox Detector (SSD) method using multi-scale feature maps for object detection, showing outstanding performance in object detection task. However, as a one-stage detection method, it’s difficult for SSD methods to quickly notice significant areas of objects in the image. In the SSD network structure, feature maps of different scales are used to independently predict object, and there is a lack of interaction between low-level feature maps and high-level feature maps. In this paper we propose an enhanced SSD method using interactive multi-scale attention features (MA-SSD). Our method uses the attention mechanism to generate attention features of multiple scales and adds it to the original detection branch of the SSD method, which effectively enhances the feature representation ability and improves the detection accuracy. At the same time, the feature of different detection scales interacts with each other, and all the detection branches in our method have a parallel structure, which ensures the detection efficiency. Our proposed method achieves competitive performance on the public dataset PascalVOC.

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.

When the general preceding vehicle detection is carried for monocular vision, the accuracy of the detection is affected by the dynamic background, illumination and occlusion. This paper proposed a preceding vehicle detection method fusing monocular and binocular vision method. Firstly, the potential area of the preceding vehicle was established by using SSD (Single Shot Multibox Detection) algorithm, then, the binocular vision-based vehicle detection method for the potential area was used, its U-V disparity was calculated, and the accurate preceding vehicle position by the vehicle depth information in the image was obtained; finally, the missed detection and false detections in the monocular vision detector were removed with the U-V disparity detections obtained by binocular vision detector. And the final results combined with the type and position information were outputted. Thereby the accuracy of the preceding vehicle detection was improved. The test result showed that on the condition of KITTI date set the visual fusion method can effectively improve the accuracy of vehicle detection. And compared with SSD algorithm in monocular, the Detection Rate was increased by 1.46%, the Recall Rate was increased by 2.83%, the Missed Rate was reduced by 2.83% and the False Rate was reduced by 1.10% than the monocular SSD algorithm.

Over the last few years, researchers have focused on computer‐aided polyp detection in gastroscopy. Deep learning (DL) has shown great promise for polyps' identification. The most exceptional contribution of DL methods in gastroenterology is their ability to identify polyps quickly and accurately using convolution neural network. Nonetheless, despite significant advancements, automatic detection of small polyps remains a challenging and complex task. Furthermore, due to multiple pooling operations, the features of small polyps are lost, resulting in low detection accuracy. This paper proposes an efficient object detection method for polyp detection using gastric images to address this issue. A single‐shot multi‐box detector (SSD) was combined with the feature extractor VGG‐16, and the Refined Map Block (RMB) was integrated into SSD's high‐resolution feature maps to get more semantic information. The RMB output was used as the input to the successive layers. The RMB comprises of attention cascade and feature map concatenation cascade. The attention cascade improved the localization accuracy, while the feature map concatenation cascade improved the classification accuracy. Using the former, the proposed attention‐based SSD for gastric polyps (ASSD‐GPNet) model focused on the specific information, a polyp, rather than the background. Furthermore, the feature map concatenation cascade adds semantic information while reducing computational complexity. The output of these two cascades was combined to produce a refined feature map that enhances the detection of small polyps. The model was trained and tested on 1970 gastric images and Pascal VOC07 + 12. Image augmentation was applied to increase the training data of gastric images to reduce overfitting and skip connections were used to overcome the vanishing gradient problem. Overall, the experimental results demonstrated that the proposed model outperformed than compared models in both medical and natural images. The ASSD‐GPNet obtained mean average precision (mAP) of 94.2% on gastric images and 76.9% on Pascal VOC.

Single Shot MultiBox Detector (SSD) is an effective method for multi-targets detection. However, due to the small scale and less information, small targets detection is a difficult assignment for SSD. In order to address this issue, we design an framework based on SSD, namely feature fusion and enhancement for SSD. First, considering that the feature information contained in different scale feature maps is different, especially the shallow feature maps which contains more detailed information and lacks semantic information, we propose a method of fusing different scale feature maps to introduce abundant object information. Secondly, although the feature maps has multiple channels, the importance of different channels' feature to the detected object is different. We introduce the feature enhancement module for effective feature enhancement. Training on the PASCAL VOC2007 and VOC2012, the proposed method has an mAP of 79.07%, which is 1.4% higher than SSD, and an FPS of 39 at the input size of 300 x300.

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

We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

The traditional traffic sign detection algorithm can not deal with the application scenarios such as intelligent transportation system or advanced assisted driving environment, and it is difficult to meet the application requirements in detection accuracy and efficiency. Focusing on the above problems, this paper proposes a traffic sign detection algorithm based on Single Shot Multibox Detector (SSD) combined with Receptive Field Module (RFM) and Path Aggregation Network (PAN). The proposed algorithm is abbreviated to SSD-RP. The SSD-RP uses the RFM to improve the receptive field and semantics of the predicted feature maps, thus improving the detection performance of small traffic signs. At the same time, the path aggregation network is introduced to integrate multiscale features, which makes the abstract semantic information and rich detailed information shared among multiscale feature maps, enhances the discrimination ability of feature system, and improves the location and classification accuracy of traffic signs. Following that, the spatial pyramid pooling module is used to pool the shallow features and integrate them into the bottom-up information transmission path of the path aggregation network, thus continuing to supplement the fine-grained features for the feature system and further improve the detection performance. The experimental results on GTSDB and CCTSDB data sets show that SSD-RP has higher mean average precision (mAP) than traditional SSD algorithm and can better detect small traffic signs, which means that SSD-RP has higher detection precision. In addition, the experimental results also show that, compared with the common object detection algorithms such as Faster R-CNN, RetinaNet, and YOLOv3, the SSD-RP achieves a better balance between detection time and detection precision.

In view of the fact that the feature layers of different scales in the traditional single shot multibox detector (SSD) are independent of each other, resulting in poor detection performance for small objects. We propose an improved SSD network for small object detection based on dilated convolution and feature fusion, which is called DFSSD. Specifically, by introducing dilated convolution, we enhance the receptive field of the third-level feature map in the network, which enables the feature map to obtain more global information. At the same time, we designed feature fusion module to fuse low-level feature map with detailed information and high-level feature map with rich semantic information. We adjust the prediction box scale of the DFSSD network prediction layer. Our proposed network obtains 78.9% mAP on PASCAL VOC2007 test at 40 FPS and 74.7% AP on difficult objects in car class of KITTI dataset. The results outperform the original SSD model by 1.4 and 1.2 points respectively.

Small bowel polyps exhibit variations related to color, shape, morphology, texture, and size, as well as to the presence of artifacts, irregular polyp borders, and the low illumination condition inside the gastrointestinal GI tract. Recently, researchers developed many highly accurate polyp detection models based on one-stage or two-stage object detector algorithms for wireless capsule endoscopy (WCE) and colonoscopy images. However, their implementation requires a high computational power and memory resources, thus sacrificing speed for an improvement in precision. Although the single-shot multibox detector (SSD) proves its effectiveness in many medical imaging applications, its weak detection ability for small polyp regions persists due to the lack of information complementary between features of low- and high-level layers. The aim is to consecutively reuse feature maps between layers of the original SSD network. In this paper, we propose an innovative SSD model based on a redesigned version of a dense convolutional network (DenseNet) which emphasizes multiscale pyramidal feature maps interdependence called DC-SSDNet (densely connected single-shot multibox detector). The original backbone network VGG-16 of the SSD is replaced with a modified version of DenseNet. The DenseNet-46 front stem is improved to extract highly typical characteristics and contextual information, which improves the model’s feature extraction ability. The DC-SSDNet architecture compresses unnecessary convolution layers of each dense block to reduce the CNN model complexity. Experimental results showed a remarkable improvement in the proposed DC-SSDNet to detect small polyp regions achieving an mAP of 93.96%, F1-score of 90.7%, and requiring less computational time.

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.

In recent years, convolutional neural network (CNN)-based methods have been extensively explored for synthetic aperture radar (SAR) target detection. Nevertheless, the convolutional sampling locations of CNNs cannot accurately fit vehicle targets due to the fixed sampling mechanism in the convolutional kernel. In this paper, we focus on the vehicle target detection task in SAR images and propose a novel rectangle-invariant rotatable convolution (RIRConv) to determine more accurately the convolutional sampling locations for vehicle targets. Specifically, this paper considers the shape characteristic of vehicle targets in SAR images, which always retain a rectangular shape despite having varying sizes, aspect ratios, and rotation angles. The proposed RIRConv equips three additional learnable attribute parameters, namely, width, height, and angle attributes, to adaptively adjust the sampling locations in the convolutional kernel according to the targets. In addition, the RIRConv applies a modulation mechanism to focus on the sampling locations that significantly affect the output. Finally, the RIRConv is introduced into the single-shot multibox detector (SSD) to realize SAR vehicle target detection. In this way, the feature representation capability of SSD for vehicle targets can be enhanced, thus leading to higher detection performance. Notably, the proposed RIRConv is “plug-and-play” and can also be used with other existing advanced technologies to achieve higher detection performance. The experiments based on the measured miniSAR data validate the effectiveness of the proposed method.

Deep learning techniques help computer vision automatically learn the intrinsic patterns within complex data. This research mainly concentrates on creating a mobile application based on augmented reality for elderly mobile users that helps in identifying the traffic signals and other signboards in real‐time using deep learning techniques. TensorFlow serves as an implementation platform to build the object detection system with deep learning. The single shot multibox detector (SSD) model and the two‐stage faster‐regional convolutional neural network (RCNN) models from TensorFlow's object detection application programming interface (API) are compared in this study. The SSD model with MobileNet as a backbone network serves well for this study as it is faster than the RCNN model with comparable accuracy. However, unconstrained environments like occlusions can be an obstacle to the effective performance of an object detection system. This research provides a solution to handle occlusions by developing a robust object detection system through image segmentation techniques. The model introduced is based on the SSD MobileNet model which enables it to be deployable on mobile devices for real‐time offline detection. The developed model exhibits faster performance than the state‐of‐the‐art instance segmentation model, Mask RCNN with comparable accuracy. Elaborated implementation of this system and results are presented in further sections.

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.