Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.Comment: Accepted by CVPR202

The Importance of Anti-Aliasing in Tiny Object Detection

Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. However, convolutional neural networks (CNNs) used as the backbone for object detection architectures typically neglect Nyquist's sampling theorem during down-sampling operations, resulting in aliasing and degraded performance. This is likely to be a particular issue for tiny objects that occupy very few pixels and therefore have high spatial frequency features. This paper applied an existing approach WaveCNet for anti-aliasing to tiny object detection. WaveCNet addresses aliasing by replacing standard down-sampling processes in CNNs with Wavelet Pooling (WaveletPool) layers, effectively suppressing aliasing. We modify the original WaveCNet to apply WaveletPool in a consistent way in both pathways of the residual blocks in ResNets. Additionally, we also propose a bottom-heavy version of the backbone, which further improves the performance of tiny object detection while also reducing the required number of parameters by almost half. Experimental results on the TinyPerson, WiderFace, and DOTA datasets demonstrate the importance of anti-aliasing in tiny object detection and the effectiveness of the proposed method which achieves new state-of-the-art results on all three datasets. Codes and experiment results are released at https://github.com/freshn/Anti-aliasing-Tiny-Object-Detection.git

Fastaer det: Fast aerial embedded real-time detection

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community

Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}

Automatic CNN channel selection and effective detection on face and rotated aerial objects

Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time.Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time

Deep Learning based Vehicle Detection in Aerial Imagery

Der Einsatz von luftgestützten Plattformen, die mit bildgebender Sensorik ausgestattet sind, ist ein wesentlicher Bestandteil von vielen Anwendungen im Bereich der zivilen Sicherheit. Bekannte Anwendungsgebiete umfassen unter anderem die Entdeckung verbotener oder krimineller Aktivitäten, Verkehrsüberwachung, Suche und Rettung, Katastrophenhilfe und Umweltüberwachung. Aufgrund der großen Menge zu verarbeitender Daten und der daraus resultierenden kognitiven Überbelastung ist jedoch eine Analyse der Luftbilddaten ausschließlich durch menschliche Auswerter in der Praxis nicht anwendbar. Zur Unterstützung der menschlichen Auswerter kommen daher in der Regel automatische Bild- und Videoverarbeitungsalgorithmen zum Einsatz. Eine zentrale Aufgabe bildet dabei eine zuverlässige Detektion relevanter Objekte im Sichtfeld der Kamera, bevor eine Interpretation der gegebenen Szene stattfinden kann. Die geringe Bodenauflösung aufgrund der großen Distanz zwischen Kamera und Erde macht die Objektdetektion in Luftbilddaten zu einer herausfordernden Aufgabe, welche durch Bewegungsunschärfe, Verdeckungen und Schattenwurf zusätzlich erschwert wird. Obwohl in der Literatur eine Vielzahl konventioneller Ansätze zur Detektion von Objekten in Luftbilddaten existiert, ist die Detektionsgenauigkeit durch die Repräsentationsfähigkeit der verwendeten manuell entworfenen Merkmale beschränkt. Im Rahmen dieser Arbeit wird ein neuer Deep-Learning basierter Ansatz zur Detektion von Objekten in Luftbilddaten präsentiert. Der Fokus der Arbeit liegt dabei auf der Detektion von Fahrzeugen in Luftbilddaten, die senkrecht von oben aufgenommen wurden. Grundlage des entwickelten Ansatzes bildet der Faster R-CNN Detektor, der im Vergleich zu anderen Deep-Learning basierten Detektionsverfahren eine höhere Detektionsgenauigkeit besitzt. Da Faster R-CNN wie auch die anderen Deep-Learning basierten Detektionsverfahren auf Benchmark Datensätzen optimiert wurden, werden in einem ersten Schritt notwendige Anpassungen an die Eigenschaften der Luftbilddaten, wie die geringen Abmessungen der zu detektierenden Fahrzeuge, systematisch untersucht und daraus resultierende Probleme identifiziert. Im Hinblick auf reale Anwendungen sind hier vor allem die hohe Anzahl fehlerhafter Detektionen durch fahrzeugähnliche Strukturen und die deutlich erhöhte Laufzeit problematisch. Zur Reduktion der fehlerhaften Detektionen werden zwei neue Ansätze vorgeschlagen. Beide Ansätze verfolgen dabei das Ziel, die verwendete Merkmalsrepräsentation durch zusätzliche Kontextinformationen zu verbessern. Der erste Ansatz verfeinert die räumlichen Kontextinformationen durch eine Kombination der Merkmale von frühen und tiefen Schichten der zugrundeliegenden CNN Architektur, so dass feine und grobe Strukturen besser repräsentiert werden. Der zweite Ansatz macht Gebrauch von semantischer Segmentierung um den semantischen Informationsgehalt zu erhöhen. Hierzu werden zwei verschiedene Varianten zur Integration der semantischen Segmentierung in das Detektionsverfahren realisiert: zum einen die Verwendung der semantischen Segmentierungsergebnisse zur Filterung von unwahrscheinlichen Detektionen und zum anderen explizit durch Verschmelzung der CNN Architekturen zur Detektion und Segmentierung. Sowohl durch die Verfeinerung der räumlichen Kontextinformationen als auch durch die Integration der semantischen Kontextinformationen wird die Anzahl der fehlerhaften Detektionen deutlich reduziert und somit die Detektionsgenauigkeit erhöht. Insbesondere der starke Rückgang von fehlerhaften Detektionen in unwahrscheinlichen Bildregionen, wie zum Beispiel auf Gebäuden, zeigt die erhöhte Robustheit der gelernten Merkmalsrepräsentationen. Zur Reduktion der Laufzeit werden im Rahmen der Arbeit zwei alternative Strategien verfolgt. Die erste Strategie ist das Ersetzen der zur Merkmalsextraktion standardmäßig verwendeten CNN Architektur mit einer laufzeitoptimierten CNN Architektur unter Berücksichtigung der Eigenschaften der Luftbilddaten, während die zweite Strategie ein neues Modul zur Reduktion des Suchraumes umfasst. Mit Hilfe der vorgeschlagenen Strategien wird die Gesamtlaufzeit sowie die Laufzeit für jede Komponente des Detektionsverfahrens deutlich reduziert. Durch Kombination der vorgeschlagenen Ansätze kann sowohl die Detektionsgenauigkeit als auch die Laufzeit im Vergleich zur Faster R-CNN Baseline signifikant verbessert werden. Repräsentative Ansätze zur Fahrzeugdetektion in Luftbilddaten aus der Literatur werden quantitativ und qualitativ auf verschiedenen Datensätzen übertroffen. Des Weiteren wird die Generalisierbarkeit des entworfenen Ansatzes auf ungesehenen Bildern von weiteren Luftbilddatensätzen mit abweichenden Eigenschaften demonstriert

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve