Drawing and Analysis of Bounding Boxes for Object Detection with Anchor-Based Models

Detecting oriented objects in optical remote sensing images has been consistently challenging due to difficulties in bounding boxes localization. The cascaded regression framework, widely employed for high-quality bounding box refinement, has demonstrated effectiveness in this domain. However, our experiments reveal a discontinuity issue in bounding box optimization in cascaded regression framework. As a result, performance gain is not guaranteed across all stages in this framework. In this paper, we propose a Distribution Discriminative Detector(DDDet) to address the above issues and enhance the optimization of bounding boxes in oriented object detection. Specifically, a novel Conditional Anchor Refinement Framework(CARF) is designed to improve cascaded regression structure. CARF distinguishes bounding boxes with different distributions, adaptively optimizing them within the well-assigned regressors. Subsequently, the Aligned Convolution Module(ACM) is integrated into each regressor, facilitating the continuous alignment between features and refined anchors. Furthermore, the Geometry-guided Training Sample Selection(GTSS) method is incorporated into CARF to assign labels based on object shape priors. Experimental results show that DDDet obtains state-of-the-art performance on mainstream datasets for oriented object detection in remote sensing image, which demonstrates the effectiveness of the proposed method. Our method surpasses many current single-stage detectors, two-stage detectors, and refine-stage detectors, achieving the mAP of 79.41% on DOTA dataset, and 44.15% on FAIR1M dataset.

Object detection is a common and challenging problem in today's world. Also due to the rapid development of deep learning employing underlying deep models over the past ten years, many researchers have investigated and contributed to improving the performance of object identification and associated tasks such as object classification, localization, and segmentation. Inference time and detection accuracy are used to rate the effectiveness of any object detector. In terms of detection accuracy, it is observed that the two-stage detectors outperform the single-stage detector however, single-stage detectors outperform their rivals in terms of inference time. Additionally, You Only Look Once (YOLO) and its versions have improved detection accuracy, some-times surpassing two-stage detectors. In this study, we performed a co-design methodology for hardware and software (HW/SW) aimed at Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) based heterogeneous platform which is Xilinx FPGA Kria KV260. A CNN-based algorithm is first extended to the YOLOv4, v5L, and v5s framework before being implemented on the Kria KV260, an out-of-the-box platform for developing advanced vision applications. A reduced computation time is observed for the deployment of the Machine Learning model on Kria KV260.

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

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption.

In this paper, we introduce the outlier detection problem in a set of uncertain points. We study two variants of the problems based upon the definition of the outlier. For a given positive integer \(k(<n)\) and a set \(\mathfrak {R}\) of n regions as the imprecise points, the first type of the outlier detection problem that we study is to locate \(n-k\) points on distinct regions, such that the size of the smallest axis-aligned bounding box (AABB), the diameter or the smallest enclosing circle (SEC) of the resulting points gets minimized. The uncertainty regions we study are squares or disks, and the excluded k regions are considered as outliers.We also study the covering versions in which the objectives of the SEC and the AABB problems are to find the smallest circle or axis-aligned bounding box, respectively, that covers the area of at least \(n-k\) regions.In the second-type of outliers, the outliers are those k regions that mostly reduce the uncertainty-induced gap between the lower bound and the upper bound on the size of the output. We give polynomial time algorithms for several variants of the mentioned problems, ranging in running time from \(O(n \log n)\) to \(O(n^{5.5}\log n)\).

It is challenging to accurately detect flexible objects with arbitrary orientation from monitoring images in power grid maintenance and inspection sites. This is because these images exhibit a significant imbalance between the foreground and background, which can lead to low detection accuracy when using a horizontal bounding box (HBB) as the detector in general object detection algorithms. Existing multi-oriented detection algorithms that use irregular polygons as the detector can improve accuracy to some extent, but their accuracy is limited due to boundary problems during the training process. This paper proposes a rotation-adaptive YOLOv5 (R_YOLOv5) with a rotated bounding box (RBB) to detect flexible objects with arbitrary orientation, effectively addressing the above issues and achieving high accuracy. Firstly, a long-side representation method is used to add the degree of freedom (DOF) for bounding boxes, enabling accurate detection of flexible objects with large spans, deformable shapes, and small foreground-to-background ratios. Furthermore, the further boundary problem induced by the proposed bounding box strategy is overcome by using classification discretization and symmetric function mapping methods. Finally, the loss function is optimized to ensure training convergence for the new bounding box. To meet various practical requirements, we propose four models with different scales based on YOLOv5, namely R_YOLOv5s, R_YOLOv5m, R_YOLOv5l, and R_YOLOv5x. Experimental results demonstrate that these four models achieve mean average precision (mAP) values of 0.712, 0.731, 0.736, and 0.745 on the DOTA-v1.5 dataset and 0.579, 0.629, 0.689, and 0.713 on our self-built FO dataset, exhibiting higher recognition accuracy and a stronger generalization ability. Among them, R_YOLOv5x achieves a mAP that is about 6.84% higher than ReDet on the DOTAv-1.5 dataset and at least 2% higher than the original YOLOv5 model on the FO dataset.

Instance segmentation needs to locate all instances in an image correctly and segment each instance precisely. Currently, the most dominant methods for instance segmentation take object detection as a pre-task. However, they rely on the accuracy of object detection incredibly. If the pre-task cannot predict an accurate bounding box, the performance of instance segmentation will degenerate. In this paper, we present a novel method for instance segmentation to solve this problem, which is called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> egmenting <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> eyond the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ounding <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ox ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S3B-Net</b> ). Our S3B-Net designs a sub-network to help instance segmentation methods based on object detection to segment the part of an instance beyond the bounding box. Specifically, the sub-network first predicts a two-dimensional pixel embedding for each pixel. Then, the Gaussian function is employed to calculate a pixel’s probability belongs to a corresponding instance according to the two-dimensional pixel embedding. Finally, the output of the sub-network combines with the output of instance segmentation based on object detection to generate a more precise instance mask. Our sub-network can easily extend on the existing instance segmentation method based on object detection to segment instance beyond the bounding box. We do our experiments on dominant instance segmentation datasets, such as the COCO dataset and Cityscapes dataset. The results show that our method can achieve 6.8 points gain compared with the baseline Mask R-CNN with ResNet-50-FPN in Cityscapes datasets, and 1.7 points gain with ResNet-101-FPN-DCN in COCO datasets. Our S3B-Net outperforms the previous state-of-the-art instance segmentation method, which proves our method is competitive. The source code of our method will be made available.

Since the emergence of deep learning in object detection in 2014, there has been rapid development and widespread application of detectors across various domains such as healthcare, road traffic, and industrial inspection. The evolution of convolutional neural network (CNN)-based detectors has reached a mature stage, with single-stage detectors playing a crucial role within CNN-based frameworks. This paper provides an analysis of the developmental process and distinctive features of classic single-stage detectors categorized into two types: Anchor-free and Anchor-based. Furthermore, the paper also provides a comparison of the accuracy trends for these two types of single-stage detectors over different years. Through this analysis and comparison, potential reasons behind the advancements in single-stage detectors during different time periods can be explored, such as network architecture optimization, increase in datasets, and algorithm improvements. Additionally, this paper offers insights into current research focal points and future prospects.

.SignificanceOptical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery.AimWe aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models.ApproachWe first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result’s confidence and center coordinates.ResultsWe implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of , suggesting that the confidence calibration on calcification detection could provide a more trustworthy result.ConclusionsGiven the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.

Object detection has received significant interest in the research field of computer vision and is widely used in human-centric applications. The occlusion problem is a frequent obstacle that degrades detection quality. In this paper, we propose a novel object detection framework targeting robust object detection in occlusion. The proposed deep learning-based network consists mainly of two parts: 1) object detection framework, which classifies the object categories and localizes the object location and 2) plug-in bounding-box (BB) estimator, which estimates the object and occlusion region from the feature map of the backbone network and the corresponding critic network for evaluating the predicted BB map. The BB estimator and the critic network are the plug-in modules added to the object detection framework and learned competitively with adversarial manner. As the plug-in BB estimator is learned to estimate the BB map containing the object and occlusion pattern information, the backbone network can embed this information to enable robust detection under occlusion in the test phase. The comprehensive experimental results on the PASCAL VOC, MS COCO, and KITTI dataset showed that the performance is improved with the plug-in BB-Critic network by predicting and criticizing object and occlusion in general generic object detection framework.

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.

This report will cover the testing of the Ballistic Research Laboratory-Computer-Aided Design's (BRL-CAD's) ray-tracer on the primitive Bag of Triangles (BoTs). Two programs were created to accommodate each component of this project. The first program created all possible triangles (BoTs) given a bounding box and step size that is specified by the user. The second program fired multiple rays toward various locations on each triangle in the database. The results of these tests were classified as a success or failure; the percentages of success were graphed against the different tolerance levels to determine the accuracy of the ray-tracer on BoTs. These data could be useful in understanding how BRL-CAD's ray-tracer performs in comparison to the Air Force's ray-tracer, FASTGEN, on BoTs.

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.

With the rapid development of artificial intelligence (AI), the application of this technology in the medical field is becoming increasingly extensive, along with a gradual increase in the amount of intelligent equipment in hospitals. Service robots can save human resources and replace nursing staff to achieve some work. In view of the phenomenon of mobile service robots' grabbing and distribution of patients' drugs in hospitals, a real‐time object detection and positioning system based on image and text information is proposed, which realizes the precise positioning and tracking of the grabbing objects and completes the grasping of a specific object (medicine bottle). The lightweight object detection model NanoDet is used to learn the features of the grasping objects and the object category, and bounding boxes are regressed. Then, the images in the bounding boxes are enhanced to overcome unfavourable factors, such as a small object region. The text detection and recognition model PP‐OCR is used to detect and recognise the enhanced images and extract the text information. The object information provided by the two models is fused, and the text recognition result is matched with the object detection box to achieve the precise positioning of the grasping object. The kernel correlation filter (KCF) tracking algorithm is introduced to achieve real‐time tracking of specific objects to precisely control the robot's grasping. Both deep learning models adopt lightweight networks to facilitate direct deployment. The experiments show that the proposed robot grasping detection system has high reliability, accuracy and real‐time performance.