PASCAL VOC Research Articles

A Neuroinspired Contrast Mechanism enables Few-Shot Object Detection

Deep learning-based object detectors often demand abundant annotated data for training. However, in practice, only limited training data are available, making Few-Shot Object Detection (FSOD) an attractive research topic. Existing two-stage proposal-based Faster R-CNN detectors for FSOD struggle to match the performance of models trained on large datasets. We argue that detectors trained with limited samples cannot establish robust comparison-based relationships. Additionally, FSOD methods only explore these relationships during the training phase. To address these issues, we draw inspiration from neuroscience studies and propose Residual Contrast Faster R-CNN (RcFRCN). RcFRCN incorporates two novel customized contrast blocks: a Residual Spatial Contrast Block and a Residual Proposal Contrast Block. These blocks capture cross-spatial and cross-proposal contrast information, enhancing both training and testing phases. We conduct comprehensive experiments on two FSOD benchmarks: PASCAL VOC and MS-COCO. Our RcFRCN achieves a mAP of 21.9 under a 30-shot setting on MS-COCO. It also achieves AP scores of 69.1, 55.8, and 64.0 under 10-shot settings of different splits on PASCAL VOC, respectively. Moreover, we apply RcFRCN on remote sensing and use our contrast blocks for open-vocabulary detection. Experiment results on these tasks also demonstrate the robustness and generalization ability of our methods.

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

IML-SSOD: Interconnected and multi-layer threshold learning for semi-supervised detection

While semi-supervised anchored detector of the R-CNN series has achieved remarkable success, semi-supervised anchor-free detector lacks the ability to generate high-quality flexible pseudo labels, resulting in serious inconsistencies in SSOD. In order to make the network learn more reliable and consistent label data to solve the problem of information bias, we propose an interconnected and multi-layer threshold learning for semi-supervised object detection (IML-SSOD). The Joint Guided Estimation (JGE) module uses the Core Zone refinement module to improve the position accuracy score of low semantic information, and combines the classification and the centerness score as evaluation criteria to predict stable labels. The multi-layer threshold filtering method selects more potential label samples for the student network ensuring the information used in training. Extensive experiments on MS COCO and PASCAL VOC datasets demonstrated the effectiveness of IML-SSOD. Compared with existing methods, our method on VOC achieved 81.9% AP50 and 57.89% AP50:95, which is highly competitive.

TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL), which trains object localization models using solely image category annotations, remains a challenging problem. Existing approaches based on convolutional neural networks (CNNs) tend to miss full object extent while activating discriminative object parts. Based on our analysis, this is caused by CNN's intrinsic characteristics, which experiences difficulty to capture object semantics at long distances. In this article, we introduce the vision transformer to WSOL, with the aim to capture long-range semantic dependency of features by leveraging transformer's cascaded self-attention mechanism. We propose the token semantic coupled attention map (TS-CAM) method, which first decomposes class-aware semantics and then couples the semantics with attention maps for semantic-aware activation. To capture object semantics at long distances and avoid partial activation, TS-CAM performs spatial embedding by partitioning an image to a set of patch tokens. To incorporate object category information to patch tokens, TS-CAM reallocates category-related semantics to each patch token. The patch tokens are finally coupled with attention maps which are semantic-agnostic to perform semantic-aware object localization. By introducing semantic tokens to produce semantic-aware attention maps, we further explore the capability of TS-CAM for multicategory object localization. Experiments show that TS-CAM outperforms its CNN-CAM counterpart by 11.6% and 28.9% on ILSVRC and CUB-200-2011 datasets, respectively, improving the state-of-the-art with large margins. TS-CAM also demonstrates superiority for multicategory object localization on the Pascal VOC dataset. The code is available at github.com/yuanyao366/ts-cam-extension.

A dataset of drilling site object detection in underground coal mines

Drilling in underground coal mine is an important measure for dealing with gas, water and hidden geological disasters, which can significantly enhance the effectiveness of disaster prevention and control in coal mining operations. In order to monitor the drilling process in real time and improve drilling efficiency, it is necessary to carry out object detection to identify and locate key targets at the drilling site. Compared with traditional object detection method, deep learning-based object detection method can improve accuracy, timeliness and stability of object detection, but it requires high-quality datasets to perform well. At present, research on object detection in underground coal mine drilling sites mainly relies on small-scale private datasets, which are insufficient for providing necessary or reliable data for deep neural network model training. In this study, we constructed a dataset of drilling site object detection using photos taken by intrinsic safety law enforcement recorders. This dataset is developed through several steps, including data cleaning, data labeling, and expert sampling verification. The mainstream YOLO series object detection model is used for data quality assessment. This dataset comprises 70,948 images from drilling sites under different environmental conditions, covering five categories of objects: gripper, chuck, coal miner, mine safety helmet, and drill pipe. It provides annotated files in PASCAL VOC format. This dataset can provide strong data support for object detection research in underground coal mine drilling sites, and plays an important role in promoting intelligent underground coal mine monitoring and early warning.

P2AT: Pyramid pooling axial transformer for real-time semantic segmentation

Recently, Transformer-based models have achieved promising results in various vision tasks, due to their ability to model long-range dependencies. However, transformers are computationally expensive, which limits their applications in real-time tasks such as autonomous driving. In addition, efficient local and global feature selection and fusion are vital for accurate dense prediction, especially driving scene understanding tasks. In this paper, we propose a real-time semantic segmentation architecture named Pyramid Pooling Axial Transformer (P2AT). The proposed P2AT takes a coarse feature from the CNN encoder to produce scale-aware contextual features, which are then combined with the multi-level feature aggregation scheme to produce enhanced contextual features. Specifically, we introduce a pyramid pooling axial transformer to capture intricate spatial and channel dependencies, leading to improved performance on semantic segmentation. Then, we design a Bidirectional Fusion module (BiF) to combine semantic information at different levels. Meanwhile, a Global Context Enhancer (GCE) is introduced to compensate for the inadequacy of concatenating different semantic levels. Finally, a decoder block is proposed to help maintain a larger receptive field. We evaluate P2AT variants on three challenging scene-understanding datasets. In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80.5%, 81.0%, 81.1% for P2AT-S, P2AT-M, and P2AT-L, respectively. Furthermore, our experiments on Cityscapes and Pascal VOC 2012 have demonstrated the efficiency of the proposed architecture. The source code will be available at https://github.com/mohamedac29/P2AT.

Steel surface defect detection algorithm in complex background scenarios

Detecting surface defects on steel poses a significant challenge attributed to factors such as poor contrast, diverse defect types, complex background clutter, and noise interference present in images of steel surface defects. Current detection techniques face challenges in quickly and accurately identifying defects within complex backgrounds. To address the deployment of high-precision detection models on edge devices with limited resources, particularly for identifying steel surface defects, this study introduces a Multi-Scale Adaptive Fusion (MSAF) YOLOv8n defect detection algorithm designed for complex backgrounds. This algorithm effectively balances detection speed and accuracy. Firstly, a Multi-Scale Adaptive Fusion Block (MS-AFB) is proposed for the extraction of multi-scale features. Secondly, a Dynamic Coordinate Attention Ghostconv Space Pooling Pyramid-fast Cross-stage Partial Convolutional (DCA-GSPPFCSPC) is devised to significantly improve detection accuracy. Furthermore, the detection head has been redesigned utilizing Lightweight Multi-scale Convolutional (LMSC) approach, and an Adaptive Pyramid Receptive Field Block (AP-RFB) has been introduced to improve the receptive field efficiently. Meanwhile, Normalized Weighted Distance (NWD) and Weighted Intersection over Union (WIoU) are employed as the boundary box loss functions, serving as substitutes for Complete Intersection over Union (CIoU) loss function with a ratio of 2:8. The experimental results obtained from the improved Northeastern University Defect Dataset (NEU-DET) dataset demonstrate that MSAF-YOLOv8n model, despite having 40.4 % of the parameters and 28.8 % of Floating Point Operations (FLOPs) of YOLOv8s, achieves a mAP@.5 that is 0.9 % higher than that of YOLOv8s. Additionally, MSAF-YOLOv8n demonstrates robust generalization capabilities in Pascal VOC2007, self-constructed datasets, and various other datasets. Subsequently, the model is implemented on embedded systems, namely Jeston TX2 NX and Orange Pi 5+, both of which demonstrate real-time detection capabilities.

Research and Application of the Median Filtering Method in Enhancing the Imperceptibility of Perturbations in Adversarial Examples

In the field of object detection, the adversarial attack method based on generative adversarial network efficiently generates adversarial examples, thereby significantly reducing time costs. However, this approach overlooks the imperceptibility of perturbations in adversarial examples, resulting in poor visual performance and insufficient invisibility of the generated adversarial examples. To further enhance the imperceptibility of perturbations in adversarial examples, a method utilizing median filtering is proposed to address these generated perturbations. Experimental evaluations were conducted on the Pascal VOC dataset. The results demonstrate that, compared to the original image, there is an increase of at least 17.2% in the structural similarity index (SSIM) for generated adversarial examples. Additionally, the peak signal-to-noise ratio (PSNR) increases by at least 27.5%, while learned perceptual image patch similarity (LPIPS) decreases by at least 84.6%. These findings indicate that the perturbations in generated adversarial examples are more difficult to detect, with significantly improved imperceptibility and closer resemblance to the original image without compromising their high aggressiveness.

FMGNet: An efficient feature-multiplex group network for real-time vision task

Lightweight network design is crucial for optimizing speed and accuracy in computer vision tasks on mobile platforms with limited resources. Widely adopted models, such as EfficientNet and RegNet have achieved significant success by integrating key elements like Pointwise Convolutions (PWConvs) and Squeeze-and-Excitation (SE) blocks. However, a notable observation is that the output feature of the PWConv closely resembles its input, particularly in the absence of an activation function. This similarity and redundancy lead to wasted computational complexity and adversely affect the inference speed. To address these issues, we propose an efficient lightweight network called Efficient Feature-Multiplex Group Network (FMGNet). FMGNet is composed of two key components: the Cross-layer Feature-multiplex Group (CFG) block and the CFG-aligned Cross-layer Attention (CCA) block. The CFG block enables more compact feature learning with fewer parameters by multiplexing the input features of the PWConv. Meanwhile, the CCA block leverages the pre-modified features derived from the CFG block’s PWConv, allowing for simultaneous and parallel channel attention modeling. Our extensive experiments across various tasks, including image classification (ImageNet), object detection (PASCAL VOC), human pose estimation (MPII), person re-identification (Market-1501, DukeMTMC-ReID, CUHK03-NP), and semantic segmentation (Cityscapes), indicate that FMGNet achieves comparable performance to state-of-the-art lightweight convolutional neural networks, offering faster inference times. Remarkably, FMGNet even surpasses recent transformer-based models, such as SwiftFormer and EfficientFormerV2, achieving superior results with lower inference latency.

DFFNet: a lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection

Steel surface defect detection is crucial in manufacturing, but achieving high accuracy and real-time performance with limited computing resources is challenging. To address this issue, this paper proposes DFFNet, a lightweight fusion network, for fast and accurate steel surface defect detection. Firstly, a lightweight backbone network called LDD is introduced, utilizing partial convolution to reduce computational complexity and extract spatial features efficiently. Then, PANet is enhanced using the Efficient Feature-Optimized Converged Network and a Feature Enhancement Aggregation Module (FEAM) to improve feature fusion. FEAM combines the Efficient Layer Aggregation Network and reparameterization techniques to extend the receptive field for defect perception, and reduce information loss for small defects. Finally, a WIOU loss function with a dynamic non-monotonic mechanism is designed to improve defect localization in complex scenes. Evaluation results on the NEU-DET dataset demonstrate that the proposed DFFNet achieves competitive accuracy with lower computational complexity, with a detection speed of 101 FPS, meeting real-time performance requirements in industrial settings. Furthermore, experimental results on the PASCAL VOC and MS COCO datasets demonstrate the strong generalization capability of DFFNet for object detection in diverse scenarios.

Nested attention network based on category contexts learning for semantic segmentation

The attention mechanism is widely used in the field of semantic segmentation, due to the fact that it can be used to obtain effective long-distance dependencies by assigning different weights to objects according to different tasks. We propose a novel Nested Attention Network (NANet) for semantic segmentation, which combines Feature Category Attention (FCA) and Channel Relationship Attention (CRA) to effectively aggregate same-category contexts in both spatial and channel dimensions. Specifically, FCA captures the dependencies between spatial pixel features and categories to achieve the aggregation of features of the same category. CRA further captures the channel relationships on the output of FCA to obtain richer contexts. Numerous experiments have shown that NANet has a lower number of parameters and computational complexity than other state-of-the-art methods, and is a lightweight model with a lower total number of floating-point operations. We evaluated the performance of NANet on three datasets: Cityscapes, PASCAL VOC 2012, and ADE20K, and the experimental results show that NANet obtains promising results, reaching a performance of 82.6% on the Cityscapes test set.

SEFANet: Semantic enhanced with feature alignment network for semantic segmentation

To address the issues of poor segmentation accuracy and insensitivity to details in semantic segmentation, this paper proposes a novel image semantic segmentation framework SEFANet. Specifically, SEFANet adopts encoder-decoder structure, and incorporates a novel perceptual enhancement mechanism called Multi-scale Spatial Integration Module (MSIM) at the encoder. MSIM is based on group convolution to boost spatial semantic features and refine spatial-gradient semantic features within the multi-scale structure. This module enhances feature extraction across different network levels, leading to improved edge detection and segmentation abilities. In the decoder, SEFANet introduces a pixel-level Interleaved Feature Alignment Module (IFAM), which leverages rich semantic information in low-dimensional features and the strategy of Semantic Offset Field. Meanwhile, IFAM warps the high-dimensional feature map into low-dimensional features, completing the calibration process through convolution operations. Experimental results on the Pascal VOC2012 val dataset and the Cityscapes val dataset confirm the effectiveness and generalization of the proposed semantic segmentation. Additionally, the results further demonstrate that SEFANet improves the poor segmentation accuracy and insensitivity to details, and achieves a competitive performance compared with other semantic segmentation methods.

Dynamic YOLO for small underwater object detection

The practical application of object detection inevitably encounters challenges posed by small objects. In underwater object detection, a crucial method for marine exploration, the presence of small objects in underwater environments significantly hampers the performance of detection. In this paper, a dynamic YOLO detector is proposed as a solution to alleviate this problem. Specifically, a light-weight backbone network is first constructed based on deformable convolution v3, with some specialized designs for small object detection. Secondly, a unified feature fusion framework based on channel-wise, scale-wise, and spatial-aware attention is proposed to fuse feature maps from different scales. This is particularly critical for detecting small objects since it allows us to fully exploit the enhanced capabilities offered by our proposed backbone network. Finally, a simple but effective detection head is designed to handle the conflict between classification and localization by disentangling and aligning the two tasks. Extensive experiments are conducted on benchmark datasets to demonstrate the effectiveness of the proposed model. Without bells and whistles, dynamic YOLO outperforms the recent state-of-the-art methods by a large margin of +0.8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,0.8$$\\end{document} AP and +1.8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,1.8$$\\end{document} APS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {AP}_{S}$$\\end{document} on the DUO dataset. Experimental results on Pascal VOC and MS COCO datasets also demonstrate the superiority of the proposed method. At last, ablation studies are conducted on DUO dataset to validate the effectiveness and efficiency of each design in dynamic YOLO. Source code will be available at https://github.com/chenjie04/Dynamic-YOLO.

Knowledge Distillation Based on Narrow-Deep Networks

Deep neural networks perform better than shallow neural networks, but the former tends to be deeper or wider, introducing large numbers of parameters and computations. We know that networks that are too wide have a high risk of overfitting and networks that are too deep require a large amount of computation. This paper proposed a narrow-deep ResNet, increasing the depth of the network while avoiding other issues caused by making the network too wide, and used the strategy of knowledge distillation, where we set up a trained teacher model to train an unmodified, wide, and narrow-deep ResNet that allows students to learn the teacher’s output. To validate the effectiveness of this method, it is tested on Cifar-100 and Pascal VOC datasets. The method proposed in this paper allows a small model to have about the same accuracy rate as a large model, while dramatically shrinking the response time and computational effort.

SpanEffiDet: Span-Scale and Span-Path Feature Fusion for Object Detection

Lower versions of EfficientDet (such as D0, D1) have smaller network structures and parameter sizes, but lower detection accuracy. Higher versions exhibit higher accuracy, but the increase in network complexity poses challenges for real-time processing and hardware requirements. To meet the higher accuracy requirements under limited computational resources, this paper introduces SpanEffiDet based on the channel adaptive frequency filter (CAFF) and the Span-Path Bidirectional Feature Pyramid structure. Firstly, the CAFF module proposed in this paper realizes the frequency domain transformation of channel information through Fourier transform and effectively extracts the key features through semantic adaptive frequency filtering, thus, eliminating channel redundant information of EfficientNet. Simultaneously, the module has the ability to compute the weights across the channels and at fine granularity, and capture the detailed information of element features. Secondly, a two-way characteristic pyramid network multi-level cross-BIFPN, which can achieve multi-layer and multi-nodes, is proposed to build cross-level information transmission to incorporate both semantic and positional information of the target. This design enables the network to more effectively detect objects with significant size differences in complex environments. Finally, by introducing generalized focal Loss V2, reliable localization quality estimation scores are predicted from the distribution statistics of bounding boxes, thereby improving localization accuracy. The experimental results indicate that on the MS COCO dataset, SpanEffiDet-D0 achieved an AP improvement of 3.3% compared to the original EfficientDet series algorithms. Similarly, on the PASCAL VOC2007 and 2012 datasets, the mAP of SpanEffiDet-D0 is respectively 1.66 and 2.65% higher than that of EfficientDet-D0.

One stage multi-scale efficient network for underwater target detection.

Due to the complexity of the underwater environment, existing methods for underwater target detection present low precision on small or dense targets. To address these issues, a novel method is proposed for underwater target detection based on YOLOv5s (You Only Look Once version 5 small), which aims to improve the precision and robustness. In this study, an efficient feature extraction network is introduced to extract significant features, and a novel attention mechanism with deformable convolution is designed to improve the feature representation. Subsequently, an adaptive spatial fusion operation is introduced at the neck of YOLOv5s to facilitate feature fusion from various layers. By integrating low-level features with high-level features, the adaptive fusion feature pyramid network effectively integrates global semantic information and decreases the semantic gap between features from various layers, contributing to the high detection precision. Comprehensive experiments demonstrate that the proposed method achieves an mAP50 of 86.97% on the Underwater Robot Professional Contest of China 2020 dataset, 3.07% higher than YOLOv5s. Furthermore, the proposed method achieves a detection precision of 76.0% on the PASCAL VOC2007 dataset, surpassing several outstanding methods.

Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map.

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a "box map" based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a "box map". In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.

Towards Stabilized Few-Shot Object Detection with Less Forgetting via Sample Normalization.

Few-shot object detection is a challenging task aimed at recognizing novel classes and localizing with limited labeled data. Although substantial achievements have been obtained, existing methods mostly struggle with forgetting and lack stability across various few-shot training samples. In this paper, we reveal two gaps affecting meta-knowledge transfer, leading to unstable performance and forgetting in meta-learning-based frameworks. To this end, we propose sample normalization, a simple yet effective method that enhances performance stability and decreases forgetting. Additionally, we apply Z-score normalization to mitigate the hubness problem in high-dimensional feature space. Experimental results on the PASCAL VOC data set demonstrate that our approach outperforms existing methods in both accuracy and stability, achieving up to +4.4 mAP@0.5 and +5.3 mAR in a single run, with +4.8 mAP@0.5 and +5.1 mAR over 10 random experiments on average. Furthermore, our method alleviates the drop in performance of base classes. The code will be released to facilitate future research.

Small object intelligent Detection method based on Adaptive Cascading Context

With the technology advances, deep learning-based object detection has made unprecedented progress. However, the small spatial ratio of object pixels affects the effective extraction of deep details features, resulting in poor detection results in small object detection. To improve the accuracy of small object detection, an adaptive Cascading Context small (ACC) object detection method is proposed based on YOLOv5. Firstly, a separate shallow layer feature was proposed to obtain more detailed information beneficial to small object detection. Secondly, an adaptive cascade method is proposed to fuse the output features of the three layers of the pyramid to adaptively filter negative semantic information, while fusing with shallow features to solve the problem of low classification accuracy caused by insufficient semantic information of shallow features. Finally, an adaptive context model is proposed to use a deformable convolution to obtain spatial context features of shallow small objects, associating the targets with the background, thereby improving the accuracy of small object detection. The experimental results show that the detection accuracy of the proposed method has been improved by 6.12%, 3.35%, 3.33%, and 5.2%, respectively, compared with the source code on the PASCAL VOC, NWPU VHR-10, KITTI, and RSOD datasets, which fully demonstrate the effectiveness of our method in small object detection.

Employing feature mixture for active learning of object detection

Active learning aims to select the most informative samples for annotation from a large amount of unlabeled data, in order to reduce time-consuming and labor-intensive manual labeling efforts. Although active learning for object detection has made substantial progress in recent years, developing an accurate and efficient active learning algorithm for object detection remains a challenge. In this paper, we propose a novel unsupervised active learning method for deep object detection. This is based on our hypotheses that an object is more likely to be wrongly predicted by the model, if the prediction changes when its feature representations are slightly mixed by another feature representations at a very small ratio. Such unlabeled samples can be regarded as informative samples that can be selected by active learning. Our method employs base representations of all categories generated from the object detection network to examine the robustness of every detected object. We design a scoring function to calculate the informative score of each unlabeled image. We conduct extensive experiments on two public datasets, i.e., PASCAL VOC and MS-COCO. Experiment results show that our approach consistently outperforms state-of-the-art single-model based methods with significant margins. Our approach also performs on par with multi-model based methods, at much lesser computational cost.