DetNet: Design Backbone for Object Detection
Recent CNN based object detectors, either one-stage methods like YOLO, SSD, and RetinaNet, or two-stage detectors like Faster R-CNN, R-FCN and FPN, are usually trying to directly finetune from ImageNet pre-trained models designed for the task of image classification. However, there has been little work discussing the backbone feature extractor specifically designed for the task of object detection. More importantly, there are several differences between the tasks of image classification and object detection. (1) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales. (2) Object detection not only needs to recognize the category of the object instances but also spatially locate them. Large downsampling factors bring large valid receptive field, which is good for image classification but compromises the object location ability. Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection. Moreover, DetNet includes the extra stages against traditional backbone network for image classification, while maintains high spatial resolution in deeper layers. Without any bells and whistles, state-of-the-art results have been obtained for both object detection and instance segmentation on the MSCOCO benchmark based on our DetNet~(4.8G FLOPs) backbone. Codes will be released.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral Classificationimage-classificationImage ClassificationInstance SegmentationObjectobject-detectionObject DetectionSemantic SegmentationvalidMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DetNet: A Backbone network for Object Detection
Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly finetune from ImageNet pre-trained models…
ClassificationGeneral Classificationimage-classificationImage Classification+6FisheyeDetNet: 360° Surround view Fisheye Camera based Object Detection System for Autonomous Driving
Object detection is a mature problem in autonomous driving with pedestrian detection being one of the first deployed algorithms. It has been comprehensively studied in the literature. However, object detection is relativ…
ARCAutonomous DrivingInstance SegmentationObject+4LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection
Remote sensing object detection has advanced rapidly with the development of large-scale benchmarks and modern detection architectures. However, existing datasets and detectors remain fragmented. Most benchmarks focus on…
Domain GeneralizationObject Detection3D-DETNet: a Single Stage Video-Based Vehicle Detector
Video-based vehicle detection has received considerable attention over the last ten years and there are many deep learning based detection methods which can be applied to it. However, these methods are devised for still …
Object Detectionvehicle detectionDeep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detecti…
Quantization