EAutoDet: Efficient Architecture Search for Object Detection

Wang, Xiaoxing; Lin, Jiale; Zhao, Jiewen; Yang, Xiaokang; Yan, Junchi

doi:10.1007/978-3-031-20044-1_38

Cited by 14 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the mAP0.5 value of our method improves by 0.8% compared with YOLOv7. However, the detection capability of our method on satellite remote sensing images is not as good as that of EAutoDet-s [62], FRIoU [63], and PCG-Net [64]. This is because MFEFNet is specifically designed for the features of UAV images, but those methods are specifically designed for the detection of satellite remote sensing images.…”

Section: Extended Experimentsmentioning

confidence: 90%

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Zhou,

Zhao,

Wan

et al. 2024

Drones

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.

show abstract

Section: Extended Experimentsmentioning

confidence: 90%

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Zhou,

Zhao,

Wan

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…The method used only 0.35 M network parameters, and the average test time per sample is about 61 ms, thus achieving a balance between performance and speed. Wang et al 29 presented an efficient framework of EAutoDet that can discover practical backbone and FPN architectures for object detection in 1.4 graphics processing unit (GPU) days. They achieved 40.1% mAP@0.5 with 120 frames per second (FPS) and 49.2% mAP@0.5 with 41.3 FPS on the COCO test data set.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Guided Evaluation Method for the High-Volume Preparation of Flexible Sensors Based on Inkjet Printing

Yao,

Wang,

Wang

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Flexible sensors for application in various industries, including biomedicine and wearable electronics, are frequently made using silver nanoparticle (AgNP) inks and inkjet printing (IJP) technology. Inkjet-printed flexible electronic devices are made up of many printed lines that run parallel to each other, and the surface morphology of the printed lines and the interline state directly impact the electrical conductivity of the electronic devices. This paper describes the experimental setup for IJP, the definition of print line characteristics, and common unavoidable defects. Conductivity and physical defects are considered in defining the print line quality assessment. In addition, two prediction models of flexible sensors before batch printing and a model for detecting defects after printing are provided. The predictive models can guide actions, leading to a print success rate of over 80%. We build the defect detection model using a neural architecture search because manually fine-tuning neural networks for reference is challenging. Finally, a target detection model with a mAP@0.5 of 81.2% is built in just 0.77 graphics processing unit (GPU) days. The model takes only 4.6 ms to detect an image, satisfying the real-time monitoring needs. At the same time, an accuracy of 95.5% can be achieved in the test data set. This work provides a new idea for the high-volume preparation of flexible sensors.

show abstract