Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

09/17/2022
by   Yu Hao, et al.
0

Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. We develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the 'tolerated' image size. We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications.

READ FULL TEXT
research
05/05/2023

DSPDet3D: Dynamic Spatial Pruning for 3D Small Object Detection

In this paper, we propose a new detection framework for 3D small object ...
research
04/18/2021

Filtering Empty Camera Trap Images in Embedded Systems

Monitoring wildlife through camera traps produces a massive amount of im...
research
02/10/2021

Scale Normalized Image Pyramids with AutoFocus for Object Detection

We present an efficient foveal framework to perform object detection. A ...
research
10/28/2021

Characterizing and Taming Resolution in Convolutional Neural Networks

Image resolution has a significant effect on the accuracy and computatio...
research
12/09/2019

Efficient Object Detection in Large Images using Deep Reinforcement Learning

Traditionally, an object detector is applied to every part of the scene ...
research
01/14/2023

Object Detection performance variation on compressed satellite image datasets with iquaflow

A lot of work has been done to reach the best possible performance of pr...
research
06/23/2022

A Novel Algorithm for Exact Concave Hull Extraction

Region extraction is necessary in a wide range of applications, from obj...

Please sign up or login with your details

Forgot password? Click here to reset