Target detection and recognition of radar spectrum image based on deep learning

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.

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

This paper proposes an improved pedestrian detection algorithm based on SSD (Single Shot MultiBox Detector) model. The algorithm mainly solves the problem that the detection effect on small-scale pedestrians is not ideal when the SSD pedestrian detection algorithm performs the pedestrian detection task. The algorithm improves the original SSD model by introducing a shallower feature map. The pedestrian detection is carried out by using the characteristics of different output layers in the model, and the multi-layer detection results are combined to improve the detection effect of small-scale pedestrian. The Squeeze-and-Excitation Module is introduced in the additional feature layer of the SSD model. The improved model can automatically acquire the importance of each channel by self-learning, according to which useful features can be enhanced and features that are not useful for current tasks can be suppressed, thereby further improving the detection ability of the algorithm for small-scale pedestrians. Experiments show that the accuracy of the proposed algorithm in the INRIA dataset reaches 93.17%, and the missed detection rate is as low as 8.72%.

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.

Aiming at the problem of target frame redundancy in the original Single Shot MultiBox Detector (SSD) model, this paper proposes a Cervical epithelial cell recognition based on improved SSD. The algorithm uses the mask to extract the ROI region, and preprocesses the original image, so that the computational complexity of the extracted image features is greatly reduced, and then the resnet network is used to replace the original VGG network. Finally, the modified SSD model is used to perform cervical epithelial cells. Identify and accurately locate. The algorithm proposed in this paper can accurately and quickly excavate the characteristics of cervical epithelial cells under microscope, and effectively improve the recognition speed and target positioning accuracy.

With the development of science and technology, robots have gradually come into our life from industry. Tennis ball collection robot is one of the more prominent representatives. How to find the position of tennis ball accurately is the key. In order to improve the speed and accuracy, we use deep learning and divide the image recognition into two steps. Using AlexNet network model to test whether there is a tennis ball in the picture. If a tennis ball exists, we then use Single Shot multibox Detector (SSD) model to locate the tennis ball, otherwise we use AlexNet to continue to check the next image. Upon verification, we can achieve the desired results.

A target detection method based on improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. Firstly, a residual network is introduced in the feature extraction structure for extracting deep features of the target; then, the aspect ratio of default boxes is redesigned to match different sizes of target detection; finally, the existing dataset is expanded with small samples based on saliency map. The experimental results based on the simulated SAR data and the real SAR data prove that the proposed method has better performance in SAR target detection.

As a typical data-driven method, deep learning relies on the availability of a huge amount of labeled data. Due to the serious lack of available samples, especially remote sensing images containing target information, the application of deep learning methods on object detection in remote sensing images is limited. Different from the common approach that tuning the deep learning model parameters to improve the detection performance, in this paper, we propose a simulation samples generating method for training the deep neural network, which can promote object detection ability for arbitrary deep learning framework. Based on the 3D model of target recovered by shape from laser scan technique and remote sensing background images, some image synthesis algorithms are employed for synthesizing multiple view 2D target images, which extends the number of images used in the training stage of a 2D object detection system. Recognition experimental results, using the detection framework of single shot multibox detector (SSD), demonstrate that Poisson image editing and Gaussian blur rendering can improve the quality of simulation remote sensing images, which significantly increases the detection performance of SSD on the various public remote sensing datasets.

Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

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.

Every two months, the Taiwan Power Company will dispatch staffs to each household to read numbers in electricity meters to calculate and collect electricity bills. However, these electricity meter staff sometimes read the wrong meter numbers and so calculate the wrong electricity bill. A system that automatically detects the digital region in electricity meter, could reduce this misreading of numbers and calculate the electricity bill correctly, thereby increasing work efficiency. Herein, the deep learning model SSD (Single Shot MultiBox Detector) is applied and fine-turned to detect the digital region in electricity meter to help the Taiwan Power Company staff. From the experimental results, it is demonstrated that the presented deep learning methods detect the digital region better than the pre-trained SSD model. In the testing experiments, the accuracies of the digital region detection are 100% for both our collected data's and fine-tuned SSD, respectively.

Purpose In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.

Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

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.