Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation

This study proposes an image data preprocessing method to improve the efficiency of transmitting and processing images for object detection in distributed IoT systems such as digital CCTV. The proposed method prepares a background image using Gaussian Mixture-based modeling with a series of continuous images in the video. Then it is used as the reference to be compared with a target image to extract the ROIs by our DSSIM-based area filtering algorithm. The background areas beside the ROIs in the image are filled with a single color—either black or white to reduce data size, or a highly saturated color to improve object detection performance. Our implementation results confirm that the proposed method can considerably reduce the network overhead and the processing time at the server side. From additional experiments, we found that the model’s inference time and accuracy for object detection can be significantly improved when our two new ideas are applied: expanding ROI areas to improve the objectness of each object in the image and filling the background with a highly saturated color.

Accurate identification of road object is crucial for intelligent traffic systems. However, due to the complexity of road traffic scenarios, developing efficient and accurate road object detection methods has been a challenging task. In this study, a new improved method for road object detection is proposed, named Enhanced YOLOv5 algorithm and Deep Schedule Object Detection (DSOD) algorithm. Real traffic scenes from the BDD100k dataset are used for training and testing the object detection model. The dataset consists of 9 different types of road objects in various traffic scenarios. The Mosaic data augmentation algorithm is applied to merge images in the dataset. Mean Average Precision (mAP), Precision (P), and Recall (R) are used as evaluation metrics to compare the enhanced YOLOv5 model with the most common models. Experimental results demonstrate that the DSOD algorithm achieves success in intelligent traffic object detection, significantly improving the accuracy and robustness of road object recognition. Additionally, the developed model shows significant performance improvement in accurately identifying objects in complex traffic scenes. These results suggest that the DSOD algorithm is a promising choice for intelligent road recognition and can easily adapt to different traffic scenarios. Furthermore, employing cloud computing for real-time detection meets the requirements of intelligent cooperative vehicles and enhances their visual perception capabilities.

A survey on object detection in optical remote sensing images

Accurate object detection is a fundamental requirement for autonomous systems to operate in dynamic urban environments. Considering the intricacy of the environment and the occlusion of objects, various point cloud based three-dimensional (3D) object detection methods have been proposed, such as point-based or voxel-based methods. In this paper, a feature selection mechanism is proposed in a voxel-based method to generate bird’s eye view (BEV) feature maps from the original point cloud. For the 3D Backbone, the U-shaped structure and single scale feature selection module are combined. After combining high-level semantics and low-level fine-grained features, the optimized features after the BEV feature maps are applied to region of interest (RoI) refinement, so that the voxel features can better serve the subsequent 3D object detection. The experimental results on KITTI dataset show higher 3D object detection accuracy compared to the state-of-the-art 3D detection methods, which reflects the effectiveness of the proposed architecture.

Sonar image object detection is essential in underwater rescue and resource exploration. Although many convolution neural network (CNN)-based object detection algorithms have achieved great success in natural images. However, for underwater sonar images, problems, such as seabed reverberation noise interference, low proportion of foreground object region pixels, and poor imaging resolution, present considerable challenges to achieving accurate underwater object detection. To address these problems, we propose a novel sonar image object detector called the multilevel feature fusion network (MLFFNet). The detector consists of multiscale convolution module (MS-Conv), multilevel feature extraction module (ML-FEM), multilevel feature fusion module (ML-FFM), neighborhood channel attention mechanism (N-CAM), multiscale feature pyramid module (MS-FPN), and feature association module (FA). First, we use the MS-Conv to extract different scale feature information in the object region. Second, the ML-FEM and ML-FFM are used to obtain the local detail and global context features. Third, the N-CAM and MS-FPN are used to obtain the foreground objects’ semantic feature and position feature, and suppress the background region noise interference. Finally, we use the FA module to enhance the category and feature correlation of different objects. Extensive experiments are conducted on the real scene sonar image dataset. The experimental results demonstrate that MLFFNet performs better than other state-of-the-art object detection methods. Code and dataset are publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/SonarMLFFNet</uri> .

Convolutional neural network based methods have provided great success in image classification and object detection tasks. However object detection, unlike the image classification task, requires much more computational intensities and energy consumption. As a result, object detection methods are difficult to integrate into embedded systems with limited resources. In this article, we propose a real-time handgun detection application on the embedded system. This application was implemented with 2 different convolutional neural network based object detection algorithms, and accuracy and speed performances were compared.

Deep learning methods for object detection in smart manufacturing: A survey

As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small object detection and most of them cannot achieve real-time processing. Therefore, this paper proposes a single-stage small object detection network (SODNet) that integrates the specialized feature extraction and information fusion techniques. An adaptively spatial parallel convolution module (ASPConv) is proposed to alleviate the lack of spatial information for target objects and adaptively obtain the corresponding spatial information through multi-scale receptive fields, thereby improving the feature extraction ability. Additionally, a split-fusion sub-module (SF) is proposed to effectively reduce the time complexity of ASPConv. A fast multi-scale fusion module (FMF) is proposed to alleviate the insufficient fusion of both semantic and spatial information. FMF uses two fast upsampling operators to first unify the resolution of the multi-scale feature maps extracted by the network and then fuse them, thereby effectively improving the small object detection ability. Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance.

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Object detection is one of the most important domains in computer vision tasks, which is an important branch of artificial intelligence. It aims at finding and locating the accurate position of objects in given pictures or videos. With the development of deep learning techniques, more powerful and robust algorithms have emerged to deal with multi-scale, high-level features to overcome the limitations of traditional pipeline of object detectors. The popularity of transformer framework enables larger capacity datasets by processing self-attention mechanism, and the object detection methods have evolved into a new era. This paper first reviews traditional object detection pipeline and brief history of deep learning, afterwards it focuses on the classification of deep learning-based object detection methods covering Convolution Neural Network based and transformer-based methods. Commonly used datasets and metrics are also covered in the next part. The Convolution Neural Network based methods mainly contain two-stage and one-stage detectors, Convolution Neural Network is the underlying structure of these methods convolutional stages are fundamental parts. Transformer-based models convert traditional object detection issues into end-to-end detection, which is widely used in dealing with images. Finally, the promising future of object detection areas are listed to show guidance on future work.

The remote sensing object detection technique has shown considerable applicability across various domains. Visible light remote sensing detection is easily affected by environmental factors such as lighting and weather conditions, resulting in poor detection performance. Infrared remote sensing detection is less affected by environmental factors, but its limited texture detail restricts its accuracy in object detection. Visible imagery and infrared imagery combined for multispectral object recognition effectively address complex remote sensing scenarios. Nonetheless, prior multispectral detection algorithms are plagued by challenges like inadequate modality information fusion, inferior performance in multi-scale object detection, and insufficient exploitation of semantic information. We propose a novel multispectral fusion architecture to tackle these difficulties. Initially, we developed the Cross-modal Adaptive Frequency Fusion (CAFF) module to independently decompose and integrate highfrequency and low-frequency information, thereby achieving fine fusion of images from different modalities in different frequency domains. Subsequently, we integrated the Multi-scale Receptive Field Aggregator (MRFA) into the SPPF module to ensure that the target obtains sufficient global contextual information while maintaining fine spatial details. We have developed a feature pyramid network for multispectral object detection, significantly improving feature interaction and fusion. This constitutes a thorough VIF-YOLO detection framework. On the VEDAI and LLVIP datasets, VIF-YOLO attains <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}_{50}$</tex> values of 75.1 % and 96.3 %, respectively, illustrating the efficacy of our approach in enhancing detection capabilities.

Object detection in videos is widely used in entertainment, robotics, surveillance etc. A major challenge in effective object detection is observed when there is occlusion, bad illumination or cluttered background. Besides, cameras do not provide any mechanism for detecting moving objects after capture. Researchers have proposed different methods for object detection in video frames, ranging from traditional to deep learning approaches. However, using the right method in the right situation for efficient and accurate detection is a concern. Even though deep learning has shown high accuracy in detection, the training and testing time and cost is a concern. In our research, we explore the best motion models for tracking objects in a video sequence. The characteristics of each model was analyzed and the results were compared using different types of videos. Our observations determine that depending on the quality of input video, traditional approaches show high accuracy in detection, comparable to state-of-the art methods.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Object detection has a great significance to remote sensing image recognition. Conventional object detection methods by using horizontal bounding box have shown good performance in general images. However, because of looking down perspective of remote sensing images, rotation bounding box is more suitable and precise for object detection. Although rotation bounding box has been researched by some works for remote sensing images, most of them are still two stages. In this paper, by improvement of YOLO architecture, we propose an one-stage object detection method to predict rotation bounding box for remote sensing images. Firstly, in the data preprocessing stage, an image cropping operation is utilized instead of zooming operation to generate suitable input data for network. Meanwhile, in order to avoid an object from being divided, an overlapping band is set during cropping. Secondly, a novel rotation bounding box representation method is introduced, and a corresponding loss function is designed in training process. Experiments on the Dota dataset demonstrate that our method outperforms state-of-the-art rotation objects detection methods, in terms of mAP, our method achieves 73.50%mAP, which is higher than methods based on rotation bounding box by 2.0%.

At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of SAR images have rarely been explored. To this end, this paper proposes a lightweight meta-learning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object detection from a more balanced perspective. First, we introduce the latest FSODM method in optical remote sensing as a benchmark framework. Second, a lightweight meta-feature extractor named DarknetS is designed to enhance the feature representation of SAR images and improve detection timeliness. Furthermore, we build a new aggregation module called AggregationS, which encodes support features and query features into the same feature subspace via a novel transformer encoder. This module design can better extract the correlation and saliency between different classes in the support set, improve the detection accuracy of the query set, and enhance the detection generalization performance of new classes. Finally, we built several real-world SAR image few-shot object detection datasets to verify the effectiveness of the method. Experimental results show that FSODS can achieve a better object detection performance compared to the baseline model under the condition that only a small amount of labelled data is required for new classes of SAR image objects.