Multi-Scale Object Detection Method Based on Multi-Branch Parallel Dilated Convolution

Basic tasks in computer vision, object detection and localization have many applications in autonomous driving, robotics, surveillance, and healthcare [1]. Deep learning has enabled previously unheard-of levels of accuracy and robustness in object detection and localization tasks, revolutionizing the area in recent years. This paper provides a thorough overview of the most recent developments and approaches in the application of deep learning algorithms for localization and object detection. First, we go over the basic ideas behind object identification and localization, emphasizing the difficulties caused by different item sizes, occlusions, and cluttered backgrounds. We then explore the nuances of deep learning architectures designed specifically for these kinds of tasks: anchor-free approaches like Center Net, single-shot detectors like YOLO, and region-based detectors like Faster R-CNN. We investigate feature extraction networks, object detecting heads, and training procedures for best performance as we investigate the underlying mechanics of these architectures. We also go over important assessment measures and datasets that are necessary for benchmarking and training object detection algorithms. We stress the significance of using strong evaluation techniques to guarantee the dependability and capacity for generalization of the suggested algorithms. Apart from technical concerns, we investigate practical uses cases where object detection and localization are essential, like autonomous vehicle pedestrian identification, surveillance system object tracking, and medical imaging anomaly detection. Lastly, we examine new developments and directions for the discipline, such as integrating multimodal data, handling domain shifts, and improving object detection algorithms' interpretability and fairness. Our goal is to stimulate further developments in computer vision systems by offering scholars, professionals, and hobbyists insightful knowledge about the problems and cutting-edge methods of object recognition and localization through this thorough review. The content of the paper is succinctly summarized in this abstract, which also highlights the significance of deep learning, object detection and localization, important methodology, datasets, applications, and future developments in the field.

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.

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 and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.

C-RPNs: Promoting object detection in real world via a cascade structure of Region Proposal Networks

In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the abilities of regionlets for modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select regions to learn the features from. The regionlet learning module focuses on local feature selection and transformation to alleviate local variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We perform ablation studies and conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets. The proposed framework outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.

This paper proposes a learnable line encoding technique for bounding boxes commonly used in the object detection task. A bounding box is simply encoded using two main points: the top-left corner and the bottom-right corner of the bounding box; then, a lightweight convolutional neural network (CNN) is employed to learn the lines and propose high-resolution line masks for each category of classes using a pixel-shuffle operation. Post-processing is applied to the predicted line masks to filtrate them and estimate clear lines based on a progressive probabilistic Hough transform. The proposed method was trained and evaluated on two common object detection benchmarks: Pascal VOC2007 and MS-COCO2017. The proposed model attains high mean average precision (mAP) values (78.8% for VOC2007 and 48.1% for COCO2017) while processing each frame in a few milliseconds (37 ms for PASCAL VOC and 47 ms for COCO). The strength of the proposed method lies in its simplicity and ease of implementation unlike the recent state-of-the-art methods in object detection, which include complex processing pipelines.

Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance the detection accuracy of both small-scale and large-scale targets in complex autonomous driving environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, the Adaptive Fusion Attention Module (AFAM) and Down-sampling Module (DownC) are introduced to increase the detection precision of small targets. Secondly, the Efficient Multi-scale Attention Module (EMA) is incorporated, enabling the model to simultaneously recognize small-scale and large-scale targets. Finally, a Minimum Point Distance IoU-based Loss Function (MPDIou-LOSS) is introduced to improve the accuracy and efficiency of object detection. Experimental validation on the KITTI dataset shows that, compared to the baseline model, the improved algorithm increased precision by 2.4%, recall by 2.6%, mAP50 by 1.5%, and mAP50-90 by an impressive 4.8%.

As the core technologies represented by Internet of Things (IoT) technology and computer vision technology booming, object detection algorithms based on deep learning have received high attention in various fields. Object detection is not only a core problem in the field of computer vision, but also a prerequisite and basis for many computer vision tasks, and has important applications in the fields of autonomous driving and video surveillance. The task of object detection is to find all the targets of interest in an image and determine their category and location. Object detection includes two tasks: object classification and object localization. However, object classification focuses on the most discriminative part of the feature map, and object localization requires a feature map focusing on the whole region of the object. Therefore, in order to improve the detection accuracy, we propose a CBAM-RetinaNet network model.CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module, which combines channel and spatial attention mechanism modules. The channel attention module focuses on the meaningful information in the input image, which would lead to an increase in the accuracy of object classification. The spatial attention module focuses on the location information of the object, which may improve the accuracy of object localization. The experiment results show that the proposed achieves better object detection performance on the PASCAL VOC2007 dataset, and the accuracy is 2% higher than that of the RetinaNet model, which has good experimental results.

Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.

LPCL: Localized prominence contrastive learning for self-supervised dense visual pre-training

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.

The task of object detection is to accurately and efficiently identify and locate a large number of predefined categories of object instances from images. With the wide application of deep learning, the accuracy and efficiency of target detection have been greatly improved. However, deep learning-based target detection still faces challenges from key technologies such as improving and optimizing the performance of mainstream target detection algorithms, improving the detection accuracy of small target objects, realizing multi-class object detection and lightweight detection model. In response to the above challenges, based on extensive literature research, this paper analyses methods for lightweight detection models and improved detection accuracy from the perspective of YOLOv5s network structure. The problems to be solved in target detection and the future research direction are predicted and prospected.

Unmanned aerial vehicle (UAV) remote-sensing images present unique challenges to the object-detection task due to uneven object densities, low resolution, and drastic scale variations. Downsampling is an important component of deep networks that expands the receptive field, reduces computational overhead, and aggregates features. However, object detectors using multi-layer downsampling result in varying degrees of texture feature loss for various scales in remote-sensing images, degrading the performance of multi-scale object detection. To alleviate this problem, we propose a lightweight texture reconstructive downsampling module called TRD. TRD models part of the texture features lost as residual information during downsampling. After modeling, cascading downsampling and upsampling operators provide residual feedback to guide the reconstruction of the desired feature map for each downsampling stage. TRD structurally optimizes the feature-extraction capability of downsampling to provide sufficiently discriminative features for subsequent vision tasks. We replace the downsampling module of the existing backbone network with the TRD module and conduct a large number of experiments and ablation studies on a variety of remote-sensing image datasets. Specifically, the proposed TRD module improves 3.1% AP over the baseline on the NWPU VHR-10 dataset. On the VisDrone-DET dataset, the TRD improves 3.2% AP over the baseline with little additional cost, especially the APS, APM, and APL by 3.1%, 8.8%, and 13.9%, respectively. The results show that TRD enriches the feature information after downsampling and effectively improves the multi-scale object-detection accuracy of UAV remote-sensing images.

Object detection is increasingly in demand in IoT service applications. Deep learning based object detection algorithms are now in fashion. As the most popular multi-scale object detection network at present, Feature Pyramid Network achieves feature augmentation by fusing features of neighboring layers. It is widely used in the most advanced object detectors to detect objects of different scales. In this paper, we propose a new attention mechanism guided bidirectional feature pyramid architecture named AgBFPN to enhance the transfer of semantic and spatial information between each feature map. We design Channel Attention Guided Fusion(CAGF) Module and Spatial Attention Guided Fusion(SAGF) Module to enhance feature fusion. The CAGF mitigates the loss of information induced by channel reduction and better transfers the semantic information from high-level to low-level features. The SAGF passes the rich spatial information of shallow features into deep features. Our experiments show that AgBFPN achieves higher Average Precision for multi-scale object detection.