MOTION COMPONENT SUPPORTED BOOSTED CLASSIFIER FOR CAR DETECTION IN AERIAL IMAGERY

Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene-adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context-aware-based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length–width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

Vehicle detection in aerial images is an important and challenging task in the field of remote sensing. Recently, deep learning technologies have yielded superior performance for object detection in remote sensing images. However, the detection results of the existing methods are horizontal bounding boxes that ignore vehicle orientations, thereby having limited applicability in scenes with dense vehicles or clutter backgrounds. In this article, we propose a one-stage, anchor-free detection approach to detect arbitrarily oriented vehicles in high-resolution aerial images. The vehicle detection task is transformed into a multitask learning problem by directly predicting high-level vehicle features via a fully convolutional network. That is, a classification subtask is created to look for vehicle central points and three regression subtasks are created to predict vehicle orientations, scales, and offsets of vehicle central points. First, coarse and fine feature maps outputted from different stages of a residual network are concatenated together by a feature pyramid fusion strategy. Upon the concatenated features, four convolutional layers are attached in parallel to predict high-level vehicle features. During training, task uncertainty learned from the training data is used to weight loss function in the multitask learning setting. For inferencing, oriented bounding boxes are generated using the predicted vehicle features, and oriented nonmaximum suppression (NMS) postprocessing is used to reduce redundant results. Experiments on two public aerial image data sets have shown the effectiveness of the proposed approach.

This paper introduces a joint feature of Fourier histograms of oriented gradients (FHOG) and local binary pattern (LBP) for vehicle detection in aerial images. Both of them are rotation invariant, so any rotation angle of vehicle in aerial images can be easily detected. A linear support vector machine (SVM) classifier is then trained over the joint feature vectors for the final vehicle detection. We evaluate our method on a public dataset and compare with some state-of-theart algorithms, the proposed joint feature outperforms them in detecting small targets in complicated backgrounds.

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.

Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

Object detection in aerial images having many practical applications in real life is becoming popular along with the surging development of deep learning and UAVs (Unmanned Aerial Vehicles). However, adverse weather conditions such as rain, night, and fog might reduce the quality of input images and significantly affect the performance of many perfectly trained detectors (detectors are trained in clear weather conditions). Moreover, object detection in aerial images often seeks high accuracy, while well-known object detection methods use horizontal bounding boxes to represent the object's location. These issues raise the inconsistency between classification and bounding box regression. Understanding the need for practical solutions, we create two experimental Fog datasets based on the original DOTA dataset to provide the in-depth analysis of fog density on multiple well-established oriented object detectors, namely Gliding Vertex, R3Det and ReDet. Furthermore, our training resources achieve promising results, flexibly ensemble to other methods to enhance models' performance and adapt to many adverse weather conditions.

Abstract. The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

This paper presents a new training model for orientation invariant object detection in aerial images by extending a deep learning based RetinaNet which is a single-stage detector based on feature pyramid networks and focal loss for dense object detection. Unlike R3Det which applies feature refinement to handle rotating objects, we proposed further improvement to cope with the densely arranged and class imbalance problems in aerial imaging, on three aspects: 1) All training images are traversed in each iteration instead of only one image in each iteration in order to cover all possibilities; 2) The learning rate is reduced if losses are not reduced; and 3) The learning rate is reduced if losses are not changed. The proposed method was calibrated and validated by comprehensive for performance evaluation and benchmarking. The experiment results demonstrate the significant improvement in comparison with R3Dec approach on the same data set. In addition to the well-known public data set DOTA for benchmarking, a new data set is also established by considering the balance between the training set and testing set. The map of losses which dropped down smoothly without jitter and overfitting also illustrates the advantages of the proposed newmodel.

Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.

Object detection in aerial images is challenging for at least two reasons: (1) most objects are small scale relative to high resolution aerial images; and (2) the object position distribution is nonuniform, making the detection inefficient. In this paper, a novel network, the coarse-grained density map network (CDMNet), is proposed to address these problems. Specifically, we format density maps into coarsegrained form and design a lightweight dual task density estimation network. The coarse-grained density map can not only describe the distribution of objects, but also cluster objects, quantify scale and reduce computing. In addition, we propose a cluster region generation algorithm guided by density maps to crop input images into multiple subregions, denoted clusters, where the objects are adjusted in a reasonable scale. Besides, we improved mosaic data augmentation to relieve foreground-background and category imbalance problems during detector training. Evaluated on two popular aerial datasets, VisDrone[29] and UAVDT[6], CDMNet has achieved significant accuracy improvement compared with previous state-of-the-art methods.

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model’s capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection.

Decision letter for "No-Extra Components Density Map Cropping Guided Object Detection in Aerial Images"

This paper presents a new, fully automatic and robust feature extraction algorithm based on the selection of a given range of scales. It compares consecutive band-pass images of a Gaussian multiscale decomposition to extract the objects that appear between given scales. The comparison is performed using original distributed entropy measures. The application to building detection in aerial images shows that scale is a robust and precise criterion for the detection of man-made objects. They also show that distributed entropic tools are relevant for the comparison of band-pass images. From a more theoretical point of view, this method stands between the scale-space and wavelet approaches. It tries to infer the geometrical concept of scale found in the scale-space theory into the algebraic scale of the wavelet theory.

A system for mine detection in aerial images is considered as an interactive system in which the operator is responsible for making iterative queries to the database of images and analyzing the results. Preliminary, each image undergoes formal decomposition into a set of feature vectors. Each feature vector is calculated for every irregularity found at a scale-invariant salient point detection stage where a blob detector is used. Assuming that every small object in the image can be described by a single invariant feature vector calculated on a patch around the salient point, formalization of search algorithms is feasible. While the template-based search is straightforward in the terms of one object - one feature vector, we focus on another important option when searching for mines - similar object search. For similar object search a hierarchical clustering algorithm is considered. The mentioned steps of image processing as well as similar object search are illustrated by performing on aerial mine field images taken by an electronic camera from a height of 27 meters. Encouraging preliminary results lead to the formulated plan for future research. The developed algorithms are planned to be used in an image search engine allowing the operator to interactively search for mines in a database of aerial images in humanitarian de-mining operations.