TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
Northeastern University（东北大学）；China Medical University（中国医科大学）；Shengjing Hospital（盛京医院）；Jinghua Hospital（京华医院）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Luebeck（吕贝克大学）
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.
Northeastern University（东北大学）；National University of Defense Technology（国防科技大学）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）；The Chinese University of Hong Kong（香港中文大学）
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities. So far, the detection of sperms and impurities still largely relies on the traditional image processing and detection techniques which only yield limited performance and often require manual intervention in the detection process, therefore unfavorably escalating the time cost and injecting the subjective bias into the analysis. Encouraged by the successes …
UCAS（中国科学院大学）；NUS（新加坡国立大学）；Pengcheng Lab（鹏城实验室）；Baidu Inc（百度公司）；ICT（中国科学院计算技术研究所）；CAS（中国科学院）；Beihang University（北京航空航天大学）；Southeast University（东南大学）；Sichuan University（四川大学）；U of Oregon（俄勒冈大学）
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection. The TinyPerson dataset was used for the TOD Challenge and is publicly released. It has 1610 images and 72651 box-levelannotations. Around 36 participating teams from the globe competed inthe 1st TOD Challenge. In this paper, we provide a brief summary of the1st TOD Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for r…
Scale-Aware Relay and Scale-Adaptive Loss for Tiny Object Detection in Aerial Images
School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Optics and Photonics, Beijing Institute of Technology（光学与 photonics 学院，北京理工大学）
Detecting tiny objects is one of the main obstacles hindering the development of object detection. The performance of generic object detectors tends to drastically deteriorate on tiny object detection tasks. In this paper, we point out that either box prior in the anchor-based detector or point prior in the anchor-free detector is sub-optimal for tiny objects. Our key observation is that the current anchor-based or anchor-free label assignment paradigms will incur many outlier tiny-sized ground truth samples, leading to detectors imposing less focus on the tiny objects. To this end, we propose…
Detecting tiny objects is a very challenging problem since a tiny object only contains a few pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory results on tiny objects due to the lack of appearance information. Our key observation is that Intersection over Union (IoU) based metrics such as IoU itself and its extensions are very sensitive to the location deviation of the tiny objects, and drastically deteriorate the detection performance when used in anchor-based detectors. To alleviate this, we propose a new evaluation metric using Wasserstein distance f…
D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统重点实验室）；Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
School of Electrical Engineering and Informatics, Institut Teknologi Bandung（万隆理工学院电气工程与信息学院）；The University of Tokyo（东京大学）；School of Life Sciences and Technology, Institut Teknologi Bandung（万隆理工学院生命科学与技术学院）；Balai Inseminasi Buatan Lembang（伦邦人工授精中心）
Background and Objective: Object detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges such as partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, and blurry objects because of the rapid movement of the sperm cells. This study proposes an architecture, called DeepSperm, that solves the aforementioned challenges and is more accurate and faster than state-of-the-art architectures. Methods: In the proposed architecture, we…
Bridging the Scale Gap: Balanced Tiny and General Object Detection in Remote Sensing Imagery
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（智能计算与信号处理重点实验室（安徽大学））；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（人工智能学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第三十八研究所）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院）
Northeastern University（东北大学）；University of Luebeck（吕贝克大学）
Image classification has achieved unprecedented advance with the the rapid development of deep learning. However, the classification of tiny object images is still not well investigated. In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks. Then we use various models of Convolutional Neural Network and Visual Transformer to conduct a series of experiments on the image dataset of tiny objects (sperms and impurities), and compare vari…
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection
Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University（电子工程系，阳明交通大学）；Institute of Electronics, National Yang Ming Chiao Tung University（电子研究所，阳明交通大学）；Department of Computer Science and Information Engineering, National Taiwan University (NTU)（计算机科学与信息工程系，国立台湾大学）
Behavior description is conducive to the analysis of tiny objects, similar objects, objects with weak visual information and objects with similar visual information, playing a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. First, we generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. Finally, we use four diff…
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny objects due to the lack of supervision from discriminative features. Our key observation is that the Intersection over Union (IoU) metric and its extensions are very sensitive to the location deviation of the tiny objects, which drastically deteriorates the quality of label assignment when used in anchor-based detectors. To tackle this problem, we propose a new evaluation metric dubbed Normalized Wasserstein Di…
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
College of Computer Science and Artificial Intelligence, Southwest Minzu University（西南民族大学计算机科学与人工智能学院）；School of Intelligent System Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；University of Warwick（华威大学）；Australian National University（澳大利亚国立大学）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；University of Toronto（多伦多大学）；University of Adelaide（阿德莱德大学）；La Trobe University（拉筹伯大学）
Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（智能计算与信号处理重点实验室（安徽大学）、教育部）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第三十八研究所）；Collaborative Innovation Laboratory for Computer Vision and Pattern Recognition, School of Artificial Intelligence and Big Data, Hefei University（计算机视觉与模式识别协同创新实验室，合肥大学人工智能与大数据学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Yale University（耶鲁大学）；Arizona State University（亚利桑那州立大学）
SNNs are an active research domain towards energy efficient machine intelligence. Compared to conventional ANNs, SNNs use temporal spike data and bio-plausible neuronal activation functions such as Leaky-Integrate Fire/Integrate Fire (LIF/IF) for data processing. However, SNNs incur significant dot-product operations causing high memory and computation overhead in standard von-Neumann computing platforms. Today, In-Memory Computing (IMC) architectures have been proposed to alleviate the "memory-wall bottleneck" prevalent in von-Neumann architectures. Although recent works have proposed IMC-bas…
