Single Shot MultiBox Detector Algorithm Research Articles

Lightweight Small-Tailed Han Sheep Facial Recognition Based on Improved SSD Algorithm

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.

Enhancing artistic analysis through deep learning: a graphic art element recognition model based on SSD and FPT.

For the analysis of art works, accurate identification of various elements of works through deep learning methods is helpful for artists to appreciate and learn works. In this study, we leverage deep learning methodologies to precisely identify the diverse elements within graphic art designs, aiding artists in their appreciation and learning process. Our approach involves integrating the attention mechanism into an enhanced Single Shot MultiBox Detector (SSD) model to refine the recognition of artistic design elements. Additionally, we improve the feature fusion structure of the SSD model by incorporating long-range attention mechanism information, thus enhancing target detection accuracy. Moreover, we refine the Feature Pyramid Transformer (FPT) attention mechanism model to ensure the output feature map aligns effectively with the requirements of object detection. Our empirical findings demonstrate that our refined approach outperforms the original SSD algorithm across all four evaluation metrics, exhibiting improvements of 1.52%, 1.89%, 3.09%, and 2.57%, respectively. Qualitative tests further illustrate the accuracy, robustness, and universality of our proposed method, particularly in scenarios characterized by dense artistic elements and challenging-to-distinguish categories within art compositions.

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

The positioning function of unmanned aerial vehicles (UAVs) is a challenging and fundamental research topic and is the premise for UAVs to realize autonomous navigation. The disappearance of satellite signals makes it challenging to achieve accurate positioning. Thus, visual positioning algorithms based on computer vision have been proposed in recent years and these algorithms have produced good results. However, these algorithms have relatively simple functions and cannot perceive the environment. Their versatility is poor, and mismatching often occurs, which affects the positioning accuracy. Aiming to address the need for integrated target recognition, target matching, and positioning of UAVs, we propose an algorithm that integrates the target recognition, matching, and positioning functions by combining the single-shot multibox detector (SSD) algorithm with the deep feature matching algorithm. This algorithm is based on the idea of pseudo-Siamese networks and the SSD algorithm, introducing a deep feature matching method to directly calculate the correspondence between two images. The main idea is to use the VGG network trained by the SSD target recognition algorithm to extract deep features, without any special training for feature matching. Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. Mismatches between the real-time and reference images are addressed by introducing the grid-based motion statistics algorithm to optimize the matching result and improve the correct matching efficiency of the target. The University-Release dataset was used to compare and analyze the performance of the proposed algorithm to verify its superiority and feasibility. The results show that the matching accuracy of the PSiamRML algorithm is generally good and that it significantly compensates for changes in the contrast, scale, brightness, blur, deformation, and so on, apart from improving the stability and robustness. Finally, a matching test scenario with aerial images captured by an S1000 six-rotor UAV served to verify the effectiveness and practicability of the PSiamRML algorithm.

Research on flame detection method based on improved SSD algorithm

A flame detection algorithm based on the improved SSD (Single Shot Multibox Detector) is proposed in response to the issues with the limited detection distance, delayed reaction, and high false alarm rate of previous flame detection systems. First, the ResNet-50-SPD model was added to the original backbone network to improve the detection of low resolution and tiny objects. After that, incorporate feature fusion between layers to improve the bond between contexts. Before the feature entered the prediction, the impact of channel number reduction was eliminated using the adaptive module AAM. According to experimental findings, the modified SSD algorithm’s mAP value on on the random division dataset and K-fold verification dataset reaches 87.89% and 89.63%, respectively, which is 3.97% and 5.17% higher than the original SSD, while the FPS remains at 64.9 f/s. It is helpful to improve the time of the fire alarm, find the ignition point in time, and better meet the actual engineering needs of fire monitoring.

Intelligence feeder system for stray cats

<span>Recently, <span lang="EN-US">the care of stray cats has become an important matter, given the difficulty for these animals to obtain food or water, whether in wild or remote areas, and this may cause the death of these animals. The aim of this work is to design and implement a low-cost feeding system for feral and indoor cats. The system is controlled by a jetson nano and arduino mega 2,560. The cat detection feeder system is built using the single shot multibox detector (SSD) MobileNet V2 algorithm. The system provides food and water for the cats. The SSD on jetson nano is implemented in real time. Jetson nano takes a picture using a webcam then runs the SSD algorithm. When a cat is detected, the arduino turns on a servo motor and a water pump. The system also includes a sim800l and GPS NEO-6M modules to send an alert message when the food and water tanks are empty. The message also contains the location of the feeder. When testing the system to determine the effectiveness of the functions, the SSD algorithm succeeded in recognizing cats, the system successfully provided food for the cats, and all parts of the device worked in high harmonic.</span></span>

Real Time Object Detection using Deep Learning and SSD Algorithm

Real time object detection is a complex area of computer vision, vast and vibrant. If there is a single object to be detected in an image, it is known as Image Localization and if there are multiple objects in an image, then it is Object Detection. Real time object identification to find and recognize real objects such as cars, bicycles, TV, flowers and people from pictures or videos. Object identification technique allows you to find out image or video details, as it allows detection, localization and multiple detection Objects in pictures or videos. Identifying objects in a video or image stream can be done through processes such as pre-processing. segmentation, foreground and background extraction, feature extraction. We will try to add new features related to the real world in future versions. In today's scenario, the Single Shot Multi-Box Detector (SSD) algorithm is considered the fastest approach for object detection using a single layer of a convolutional network. In our paper, we have focused on enhancing the classification accuracy of object detection while maintaining the algorithm's speed. Experimental results have confirmed that our proposed improved SSD algorithm achieves high accuracy.

Deep Learning-Based Automatic Defect Detection Method for Sewer Pipelines

To address the issues of low automation, reliance on manual screening by professionals, and long detection cycles in current urban drainage pipeline defect detection, this study proposes an improved object detection algorithm called EFE-SSD (enhanced feature extraction SSD), based on the SSD (Single Shot MultiBox Detector) network. Firstly, the RFB_s module is added to the SSD backbone network to enhance its feature extraction capabilities. Additionally, multiple scale features are fused to improve the detection performance of small target defects to some extent. Then, the improved ECA attention mechanism is used to adjust the channel weights of the output layer, suppressing irrelevant features. Finally, the Focal Loss is employed to replace the cross-entropy loss in the SSD network, effectively addressing the issue of imbalanced positive and negative samples during training. This increases the weight of difficult-to-classify samples during network training, further improving the detection accuracy of the network. Experimental results show that EFE-SSD achieves a detection mAP of 92.2% for four types of pipeline defects: Settled deposits, Displaced joints, Deformations, and Roots. Compared to the SSD algorithm, the model’s mAP was increased by 2.26 percentage points—ensuring the accuracy of pipeline defect detection.

Single shot multibox detector object detection based on attention mechanism and feature fusion

The single shot multibox detector (SSD) is one of the most important algorithms in single-stage target detection, having the characteristics of multiscale detection and rapid detection speed. However, the effective SSD feature layers are independent of one another, which can lead to object detection difficulties. To address this problem, we proposed an improved SSD object detection algorithm. First, the global attention mechanism (GAM)—which can enhance spatial and channel information—was introduced into the multiscale feature layer. The channel attention module of the GAM was improved. Second, a feature fusion module was introduced to strengthen the relationship between feature layers. Finally, the cross stage partial structure was introduced into the feature fusion module, and used to improve the model’s learning ability. For model training and detection based on the PASCAL VOC dataset, the mean average precision and frames per second obtained by the improved SSD algorithm were 84.67% and 18.67, respectively, which could effectively detect difficult targets.

Driver identification and fatigue detection algorithm based on deep learning.

In order to avoid traffic accidents caused by driver fatigue, smoking and talking on the phone, it is necessary to design an effective fatigue detection algorithm. Firstly, this paper studies the detection algorithms of driver fatigue at home and abroad, and analyzes the advantages and disadvantages of the existing algorithms. Secondly, a face recognition module is introduced to crop and align the acquired faces and input them into the Facenet network model for feature extraction, thus completing the identification of drivers. Thirdly, a new driver fatigue detection algorithm based on deep learning is designed based on Single Shot MultiBox Detector (SSD) algorithm, and the additional layer network structure of SSD is redesigned by using the idea of reverse residual. By adding the detection of drivers' smoking and making phone calls, adjusting the size and number of prior boxes of SSD algorithm, improving FPN network and SE network, the identification and verification of drivers can be realized. The experimental results showed that the number of parameters decreased from 96.62 MB to 18.24 MB. The average accuracy rate increased from 89.88% to 95.69%. The projected number of frames per second increased from 51.69 to 71.86. When the confidence threshold was set to 0.5, the recall rate of closed eyes increased from 46.69% to 65.87%, that of yawning increased from 59.72% to 82.72%, and that of smoking increased from 65.87% to 83.09%. These results show that the improved network model has better feature extraction ability for small targets.

Target Detection Algorithm Incorporating Visual Expansion Mechanism and Path Syndication

Because the lack of semantic information exchange between characteristic layers, the SSD (Single Shot multibox Detector) algorithm has insufficient detection performance. To address this problem, a detection algorithm called VPE-SSD (Visual Path Enhancement SSD) by incorporating a visual expansion mechanism and path syndication proposed in this paper. Firstly, a visual expansion mechanism is added to the shallow characteristic layer to increase the perceptual field. This enables the semantic information in the shallow layer to be more fully utilized by the network. It can also achieve the purpose of enhancing the expressiveness of the shallow feature layer. Then, the processed deep and shallow characteristic layers are fed into the path syndication module for bi-directional fusion. This improves the global information of the feature layers and generates multi-scale global feature maps. Next, to enhance the detailed information of deep characteristics and improve their expression, the deep characteristic enhancement module is applied to the last three characteristic maps. Finally, using the blended attention module to reduce the negative interference and improve the expression of characteristic maps during target detection. The experimental analysis of the VPE-SSD algorithm is conducted on VOC and COCO, and the mAP is 83.4% and 48.4%. From the result, VPE-SSD algorithm can make better use of the different size characteristic information which helps to improve the performance of the algorithm.

Research on Small Target Detection Technology Based on the MPH-SSD Algorithm.

To address the problems of less semantic information and low measurement accuracy when the SSD (single shot multibox detector) algorithm detects small targets, an MPH-SSD (multiscale pyramid hybrid SSD) algorithm that integrates the attention mechanism and multiscale double pyramid feature enhancement is proposed in this paper. In this algorithm, firstly, the SSD algorithm is used to extract the feature map of small targets, and the shallow feature enhancement module is added to expand the receptive field of the shallow feature layer so as to enrich the semantic information in the feature layer for small targets and improve the expression ability of shallow features. The processed shallow feature layer and deep feature layer are fused at multiple scales, and the semantic information and location information are fused together to obtain a feature map with rich information. Secondly, the cascaded double pyramid structure is used to transfer from the deep layer to the shallow layer so that the context information between different feature layers can be effectively transferred and the feature information can be further strengthened. The hybrid attention mechanism can retain more context information in the network, adaptively adjust the feature map after addition and fusion, and reduce the background interference. The experimental analysis of MPH-SSD algorithm on Pascal VOC and MS COCO datasets shows that the map of this algorithm is 87.7% and 51.1%, respectively. The results show that the MPH-SSD algorithm can make better use of the feature information in the shallow feature layer in the process of small target detection and has better detection performance for small targets.

Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm

With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the crushing mouth. Based on deep learning (DL) and machine vision (MV) technology, an on-line detection method is studied to trace the blockage in a swift and accurate manner, so that the corresponding detection system can be designed accordingly. The charge coupled device (CCD) industrial camera set above the crushing mouth is used to collect images and input them to the edge computing equipment. The original Single Shot MultiBox Detector (SSD) preprocessing model is trained and optimized before it is combined with the MV technology to detect and then the MV technology is combined to detect whether the crushing mouth is covered. In Ansteel Group GUANBAOSHAN mine, the accuracy of recognition and detection system with human observation was examined for one month, and the tested accuracy is 95%. The experimental results show that the proposed method can detect the crushing mouth blockage in real time, which would solve the problem that the blockage can only be identified by human eyes in traditional method, and provides basic support for the unattended crushing station.

Detection of invasive <italic>Saccharomyces cerevisiae</italic> infections based on convolutional neural network

<p indent="0mm"><italic>Saccharomyces cerevisiae </italic>(<italic>S</italic>.<italic> cerevisiae</italic>) is common in the baking and brewing industries as a fermenting strain and used as a probiotic for preventing and treating various diarrheal and related diseases. However, the cases of invasive <italic>S</italic>.<italic> cerevisiae</italic> infections due to the ingestion of <italic>S</italic>.<italic> cerevisiae</italic> have been increasing over the last two decades, especially in the elderly, immunocompromised, and critically ill patient populations. Additionally, current clinical methods of diagnosing invasive<italic> S</italic>.<italic> cerevisiae</italic> infections are time-consuming, while the optimal times for the diagnosis and early prevention are easily missed. In this paper, a convolutional neural network (CNN)-based object detection method is proposed for the detection and identification of <italic>S</italic>.<italic> cerevisiae</italic> cells in the blood for invasive <italic>S</italic>.<italic> cerevisiae</italic> disease diagnosis. The method is based on the single shot multibox detector algorithm using the ResNet-50 network as a feature extraction network. The experimental results show that the recognition accuracy of the method can reach 97.70%, with a detection time of <sc>0.31 s.</sc> Moreover, the algorithm recognition accuracy showed a significant advantage over three similar CNN algorithms. The paper verifies that the CNN-based object detection method can be a novel method for the detection and diagnosis of invasive <italic>S</italic>.<italic> cerevisiae</italic> infections, and the advantages of high detection accuracy and short method time can achieve early prevention and timely diagnosis of invasive <italic>S</italic>.<italic> cerevisiae</italic> infections.

Crack detection for concrete bridges with imaged based deep learning.

Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.

Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD

The detection of Lingwu long jujubes in a natural environment is of great significance for robotic picking. Therefore, a lightweight network of target detection based on the SSD (single shot multi-box detector) is presented to meet the requirements of a low computational complexity and enhanced precision. Traditional object detection methods need to load pre-trained weights, cannot change the network structure, and are limited by equipment resource conditions. This study proposes a lightweight SSD object detection method that can achieve a high detection accuracy without loading pre-trained weights and replace the Peleenet network with VGG16 as the trunk, which can acquire additional inputs from all of the previous layers and provide itself characteristic maps to all of the following layers. The coordinate attention module and global attention mechanism are added in the dense block, which boost models to more accurately locate and identify objects of interest. The Inceptionv2 module has been replaced in the first three additional layers of the SSD structure, so the multi-scale structure can enhance the capacity of the model to retrieve the characteristic messages. The output of each additional level is appended to the export of the sub-level through convolution and pooling operations in order to realize the integration of the image feature messages between the various levels. A dataset containing images of the Lingwu long jujubes was generated and augmented using pre-processing techniques such as noise reinforcement, light variation, and image spinning. To compare the performance of the modified SSD model to the original model, a number of experiments were conducted. The results indicate that the mAP (mean average precision) of the modified SSD algorithm for object inspection is 97.32%, the speed of detection is 41.15 fps, and the parameters are compressed to 30.37% of the original networks for the same Lingwu long jujubes datasets without loading pre-trained weights. The improved SSD target detection algorithm realizes a reduction in complexity, which is available for the lightweight adoption to a mobile platform and it provides references for the visual detection of robotic picking.

MFFAMM: A Small Object Detection with Multi-Scale Feature Fusion and Attention Mechanism Module

Aiming at the low detection accuracy and poor positioning for small objects of single-stage object detection algorithms, we improve the backbone network of SSD (Single Shot MultiBox Detector) and present an improved SSD model based on multi-scale feature fusion and attention mechanism module in this paper. Firstly, we enhance the feature extraction ability of the shallow network through the feature fusion method that is beneficial to small object recognition. Secondly, the RFB (Receptive Field block) is used to expand the object’s receptive field and extract richer semantic information. After feature fusion, the attention mechanism module is added to enhance the feature information of important objects and suppress irrelevant other information. The experimental results show that our algorithm achieves 80.7% and 51.8% mAP on the PASCAL VOC 2007 classic dataset and MS COCO 2017 dataset, which are 3.2% and 10.6% higher than the original SSD algorithm. Our algorithm greatly improves the accuracy of object detection and meets the requirements of real-time.

Sea Cucumber Detection Algorithm Based on Deep Learning.

The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these problems, we proposed an improved algorithm for sea cucumber detection based on the traditional SSD algorithm. MobileNetv1 is selected as the backbone of the SSD algorithm. We increase the feature receptive field by receptive field block (RFB) to increase feature details and location information of small targets. Combined with the attention mechanism, features at different depths are strengthened and irrelevant features are suppressed. The experimental results show that the improved algorithm has better performance than the traditional SSD algorithm. The average precision of the improved algorithm is increased by 5.1%. The improved algorithm is also more robust. Compared with YOLOv4 and the Faster R-CNN algorithm, the performance of this algorithm on the P-R curve is better, indicating that the performance of this algorithm is better. Thus, the improved algorithm can stably detect sea cucumbers in real time and provide reliable feedback information.

Improved SSD network for fast concealed object detection and recognition in passive terahertz security images

With the strengthening of global anti-terrorist measures, it is increasingly important to conduct security checks in public places to detect concealed objects carried by the human body. Research in recent years has shown that deep learning is helpful for detecting concealed objects in passive terahertz images. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our research aims to propose a novel method for accurate and real-time detection of concealed objects in terahertz images. To reach this goal we trained and tested a promising detector based on deep residual networks using human image data collected by passive terahertz devices. Specifically, we replaced the backbone network of the SSD (Single Shot MultiBox Detector) algorithm with a more representative residual network to reduce the difficulty of network training. Aiming at the problems of repeated detection and missed detection of small targets, a feature fusion-based terahertz image target detection algorithm was proposed. Furthermore, we introduced a hybrid attention mechanism in SSD to improve the algorithm’s ability to acquire object details and location information. Finally, the Focal Loss function was introduced to improve the robustness of the model. Experimental results show that the accuracy of the SSD algorithm is improved from 95.04 to 99.92%. Compared with other current mainstream models, such as Faster RCNN, YOLO, and RetinaNet, the proposed method can maintain high detection accuracy at a faster speed. This proposed method based on SSD achieves a mean average precision of 99.92%, an F1 score of 0.98, and a prediction speed of 17 FPS on the validation subset. This proposed method based on SSD-ResNet-50 can provide a technical reference for the application and development of deep learning technology in terahertz smart security systems. In the future, it can be widely used in some public scenarios with real-time security inspection requirements.

An IoT-Based Breeding Egg Identification and Coding System for Selection of High-Quality Breeding Geese.

Simple SummaryIn the process of breeding geese, manually recording data causes the problems of missing and confusing characterization data; furthermore, manual intervention can lead to the stress response of breeding geese and affect the laying efficiency of breeding geese. In this study, we tried to combine the Internet of things and computer image technology to improve the accuracy of data recording, so as to achieve an accurate correspondence between breeding goose individual data and egg-laying data. Therefore, we developed and tested a breeding egg identification and coding system to realize the selection of high-quality breeding geese. The test results showed that the system realized a correspondence of 97.8% between breeding goose individual data and egg-laying data. The system realized the information recording of breeding geese under nonmanual intervention, realized the correspondence between individual data and egg-laying data, and improved the health and welfare of breeding geese.The selection of breeding geese requires the recording of egg production information to correspond to the identity of the breeding geese. However, due to the special physiological characteristics of breeding geese, manual recording in practice can affect the egg-laying performance of breeding geese and can also lead to problems of missing and confusing individual breeding goose data with the number of eggs laid by the geese. For contactless recording of breeding goose identity and egg production information for high-quality breeding, this paper proposes an Internet of things (IoT)-based breeding egg identification and coding method for the selection of high-quality breeding geese. At the sensing level, we deployed a radiofrequency identification (RFID)-based sensor. Each breeding goose wore a foot ring RFID tag on its leg, and the individual information was read by foot ring RFID readers placed at the bottom of the devices. Individual information was uploaded to the cloud server for database management through structured query language (MySQL). The target detection modules were mounted on top of the devices, and the breeding geese and eggs were detected in the delivery rooms by an improved single-shot multi-box detector (SSD) target detection algorithm. The egg body limit transmission device and contactless coding device were activated only in the case of breeding eggs, and the breeding goose information was printed on the egg bodies in the form of quick response codes (QR codes), which enabled the breeding egg information to correspond with the breeding goose information. An evaluative experiment was performed using a system for the selection of high-quality breeding geese, with web cameras and a cloud monitoring platform. The breeding geese were allowed 14 days to become accustomed to the experimental environment before monitoring began. The evaluative experiment results showed that the pass rate of egg body coding reached 98.25%, the improved SSD algorithm was 8.65% more accurate and 62.6 ms faster than traditional SSD, and the accuracy rate corresponding to the individual information of the breeding geese and the surface information of the goose eggs was 97.8%. The experimental results met the requirements of accurate marking of individual information of breeding geese, which can provide technical support for the selection of high-quality breeding geese.

Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network.

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.