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Semi-supervised object detection aims to enhance object detectors by utilizing a large number of unlabeled images, which has gained increasing attention in natural scenes. However, when these methods are directly applied to scenes with tiny objects, they face the challenge of selecting pseudo-labels with high localization quality due to the minuscule and blurred characteristics of these objects. To address this issue, we propose a novel method called semi-supervised tiny object detection (STOD). Firstly, to enhance the localization accuracy of pseudo-labels, we design a dense IoU-aware head that evaluates the quality of bounding box localization by incorporating additional predicted overlap values. Secondly, to mine more potential pseudo-labels, we propose a GMM-based multi-threshold pseudo-labels mining module that dynamically generates multiple thresholds using classification scores and overlap values to classify bounding boxes into strong positive and weak positive pseudo-labels. Lastly, we design the localization-aware weighting loss to incorporate the localization quality of both positive and negative samples in order to enhance the accuracy of pseudo-label localization. The experimental results show that STOD achieves comparable performance when compared to both fully and semi-supervised methods. Notably, on the VisDrone-Partial benchmark, STOD achieves outstanding results by outperforming our baseline model with improvements of 3.1 mAP, 5.7 A P 50 , and 3.0 A P 75 . [ABSTRACT FROM AUTHOR]

Semi-supervised object detection helps to monitor and manage maritime transportation effectively, saving labeling costs. Currently, many semi-supervised object detection methods use a combination of data augmentation and pseudo-label to improve model performance. However, these methods may get into trouble in complex maritime scenes, including occlusion, scale variations and lighting variations, leading to distribution bias between labeled data and unlabeled data and pseudo-label bias. To address these problems, we propose a semi-supervised object detection method in complex maritime scenes based on adaptive adversarial self-training, which provides a teacher–student detection framework to use a robust pseudo-label with data augmentation. The proposed method contains two modules called adversarial distribution discriminator and label adaptive assigner. The adversarial distribution discriminator is proposed to match the distribution between augmented data generated from different data augmentations, while the label adaptive assigner is proposed to reduce the labeling bias for unlabeled data so that the pseudo-label of unlabeled data contributes to the detection performance effectively. Experimental results show that the proposed method achieves a better mean average precision of 91.4%, with only 5% of the labeled samples compared with other semi-supervised object detection methods, and its detection speed is 11.1 frames per second. Experiments also demonstrate that the proposed method improves the detection performance compared with fully supervised detectors. [ABSTRACT FROM AUTHOR]

Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named "Wheat Teacher", which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection. [ABSTRACT FROM AUTHOR]

Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search and rescue (SaR) system. However, many problems have not been effectively solved in existing remote-vision-based SaR systems, such as the shortage of person samples in SaR scenarios and the low tolerance of small objects for bounding boxes. To address these issues, a copy-paste mechanism (ISCP) with semi-supervised object detection (SSOD) via instance segmentation and maximum mean discrepancy distance is proposed (MMD), which can provide highly robust, multi-task, and efficient aerial-based person detection for the prototype SaR system. Specifically, numerous pseudo-labels are obtained by accurately segmenting the instances of synthetic ISCP samples to obtain their boundaries. The SSOD trainer then uses soft weights to balance the prediction entropy of the loss function between the ground truth and unreliable labels. Moreover, a novel evaluation metric MMD for anchor-based detectors is proposed to elegantly compute the IoU of the bounding boxes. Extensive experiments and ablation studies on Heridal and optimized public datasets demonstrate that our approach is effective and achieves state-of-the-art person detection performance in aerial images. [ABSTRACT FROM AUTHOR]