Multiobject Detection Algorithm Based on Adaptive Default Box Mechanism

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.

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.

SSD (Single Shot Multi Box Detector) is an object detection algorithm based on deep learning. As one of the most mainstream detection algorithms, it can greatly improve the detection speed and ensure the detection accuracy. In this paper, the Batch Norm operation is added to the network in order to improve the generalization of the network and speed up network training. The object counting function is added to the image recognition. This paper uses SSD algorithm that incorporates Batch Norm algorithm. The object detection system was built by the Flask framework and the Layui framework. The system can select the data to be detected on the front-end page, the detection results and the number of each type of object were displayed on the front-end page in real time.

Potholes are cracks on the road’s surface that leave a hole behind it. Reporting potholes to accountable bodies at an early stage can save many lives. Therefore, timely inspection and maintenance of potholes are required for smooth transportation. Traditional pothole detection methods are labor-intensive and time-consuming. This research focuses on such gaps and presents an intelligent detecting system that uses a smartphone camera, sensors, and gyroscope for real-time detection of potholes. The proposed model covers two essential functions: i). automated identification of potholes, and ii). notifying users to escape probable accidents. The “Single Shot Multi-Box Detector (SSD)” technique trains the pothole image datasets. For developing the dataset, pothole images are taken and labeled with TensorFlow object detection API. The method achieved 90% accuracy in detecting potholes in used image datasets. Study outcomes can help stakeholders get road profiling information and alerts about potholes for smooth road transportation.

An intelligent water level monitoring method based on SSD algorithm

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network’s ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader.

Single shot multibox detector (SSD) is a state-of-the-art network for real-time object detection. It is originally designed for general datasets. While, for specific datasets, their distribution of ground truth boxes is somehow different and thus, SSD shows unsatisfying performance. In this paper, we improve the performance of SSD on specific datasets. We first dissect the mechanism of predictors, the predicting parameters of a potential detection, in two aspects: classification and localization. Then we reveal the relationship between default boxes and predictors. With this point we finally make an improvement on default box setting and achieve a higher mAP over the original SSD on specific datasets.

With the continuous development of technology, the application of computer vision technology in cost monitoring of construction projects is becoming increasingly important. By utilizing computer vision technology, construction companies can monitor the progress of construction sites, material usage, and allocation of human resources in real-time, thereby better controlling project costs. The application of this technology can not only improve the efficiency of construction projects, but also reduce human errors and waste, which is of great significance for the successful completion of projects. Therefore, construction companies should actively adopt computer vision technology to improve the efficiency and accuracy of cost monitoring. In recent years, China's economy has continued to grow, continuously driving the development of infrastructure and manufacturing industries. However, at the same time, the production safety situation in China is becoming increasingly severe, and safety accidents continue to occur. One of the main causes of safety accidents is human misconduct, which includes workers wearing uniforms and safety helmets improperly during construction. Workers engage in some dangerous behaviors during construction, such as making phone calls, falling, and squatting for long periods of time. In response to the above issues, this article studies how the SSD (Single Shot MultiBox Detector) algorithm, CNN (Convolutional Neural Networks) algorithm, and YOLO (You only look once) algorithm can be applied to the behavior detection of construction workers, and compares them with existing object detection algorithms through experiments. The experimental data shows that the AP (Average precision), AP50, and AP75 values of the SSD-CNN algorithm in this article are 51.29%, 69.85%, and 54.81%, respectively. Among all the algorithms, the values of the three indicators rank in the top few.

In recent years, with the rapid development of satellite technology, object detection technology in remote sensing images has become an important research direction in the field of image processing. In this paper, aiming at the problems of low resolution, slow detection speed and vulnerability to background interference in remote sensing satellite images of ground vehicles, a vehicle small object detection algorithm based on residual network is proposed. Based on the original SSD (Single Shot MultiBox Detector) algorithm, the backbone network adopts the residual network with stronger feature extraction ability and less parameters, which can effectively improve the detection accuracy of small targets; Meanwhile, the Dense Extra Feature Layers network is proposed to enhance Feature propagation. Experimental results show that the mAP value of the proposed method in vehicle remote sensing image detection reaches 0.573, and the detection speed is 30.1fps, which has a good improvement in the detection accuracy and speed.

The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.

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.

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

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.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

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.