An Anchor-Free Lightweight Object Detection Network

Received 2 August 2023, accepted 24 September 2023, date of publication 4 October 2023, date of current version 11 October 2023.

Digital Object Identifier 10.1109/ACCESS.2023.3321966

An Anchor-Free Lightweight Object

Detection Network

WEINA WANG AND YUNYAN GOU

College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China

Corresponding author: Weina Wang (wangweina@jlict.edu.cn)

This work was supported in part by the Natural Science Foundation of China under Grant 62266046; in part by the Natural Science

Foundation of Jilin Province, China, under Grant YDZJ202201ZYTS603; and in part by the Natural Science Foundation of

improves the detection capability of small objects. Besides, a label assignment strategy is proposed to

determine the prominent feature, and a center correction mechanism is introduced to make the predicted

bounding box closer to the ground truth to further improve the detection accuracy. Extensive experiments

are conducted on MS COCO and Pascal VOC datasets, and the results demonstrate that the proposed method

achieves better results than the existing detection methods on detection accuracy by increasing 0.5% on the

MS COCO dataset, and has a detection accuracy increase of 0.4% on the Pascal VOC dataset, which proves

the superiority of the proposed method.

INDEX TERMS Object detection, anchor-free, lightweight, small object, label assignment strategy.

Object detection task is to find out all the objects of interest

[9], [10], text detection [11], [12] traffic detection [13], [14].

Due to boom in deep learning, the object detection algorithm

has been developed rapidly. Typical methods are Faster R-

CNN [15] and fully convolutional networks (FCN) [16].

They have the advantages of conceptual intuitiveness, good

flexibility, robustness, and rapidity of training and reasoning.

In addition, feature pyramid networks (FPN) [17] use

multi-stage image pyramids to obtain feature pyramids,

which improved the average accuracy of the detector.

Focal loss [18] focuses on sparse hard samples and prevented

To overcome the shortcoming of anchor-based detectors,

anchor-free detection is proposed. The anchor-free detector

utilizes pixel-by-pixel prediction to solve the detection

problem. The detector need not require predetermined anchor

points, avoiding complex anchor point calculations, and

greatly reducing computation time and computational power.

Therefore, anchor-free detection has gained great momentum

in the object detection field. Cornernet [19] converts the

network’s focus on the object bounding box into a pair

of key points without designing anchor points, which

of the object. Although the method effectively improves

the accuracy and recall, the detection with occlusion is

not effectively solved because of mutual occlusion for the

object. Fully convolutional one-stage (FCOS) [21] is a pixel-

by-pixel object detection model, which utilizes centrality

center-ness to improve the accuracy. Compared with anchor-

based methods, FCOS has achieved promising performance

in detection accuracy and speed. However, it still has

performance. ShuffleNet [23] is specially designed for

mobile devices, which have fewer parameters and efficient

computation. The traditional approaches to lightening the

network are to use simplification, pruning, and compression.

MobileNet [24], [25], [26] series discard these approaches

and use the efficient network architecture. MobileNets not

only have the advantage of less computation, but also can

ensure the accuracy of detection. Inspired by the structure

of MobileNets, the proposed method introduces the efficient

architecture as the backbone network to lighten the network.

of small objects. Finally, a label assignment strategy is

proposed, and then the strategy is combined center correction

mechanism to improve the limited detection performance

due to the lack of accurate label assignment. The main

contributions of this paper are as follows:

1) A small object enhancement module is constructed

based on the single stage headless face detector. The module

can improve the focus on small objects, and enhance the

detection capability.

2) A label assignment strategy is proposed to select the

abnormal bad cases, and the predicted index can be further

optimized.

4) An anchor-free lightweight object detection network

is proposed, which can reduce the computational cost and

improve the accuracy of detection. Meanwhile, the proposed

network can effectively detect small objects.

5) A comprehensive evaluation of the proposed method

on MS COCO and Pascal Voc detection benchmarks.

The experimental results not only demonstrate that the

proposed detector has better detection accuracy than the other

detectors, but also show the parameters of the model are

The structure of this paper is as follows: Section II

visualizations are carried out to demonstrate the superiority

of the proposed model. Our work is summarized, and the

research direction of follow-up work is pointed out in

Section V.

The workflow of the anchor-based object detector can be

summarized as follows: Firstly, a set of predefined anchors

is identified, and the image is divided into regions. Then,

each candidate box is classified with a classifier to determine

uses the search method to construct candidate bound, but the

speed is still insufficient for real-time requirements. Faster

R-CNN [15] utilizes a fully convolutional network as a

region suggestion network. The network generates the corre-

sponding candidate windows with associated object scores,

which can determine the probability of the appearance of

an object. Compared with one-stage networks, the two-stage

Faster R-CNN model is more advantageous in objecting

high-precision and multi-scale detection. SSD [29] uses

several different detection branches to detect multiple scales

W. Wang, Y. Gou: Anchor-Free Lightweight Object Detection Network

To overcome the above drawbacks, the anchor-free detector

was proposed.

probability. DenseBox [35] uses a circular region at the

we propose a lightweight backbone network, a small object

enhance the detection performance.

propagation to alleviate the obstacles caused by incomplete

object scanning in autonomous driving scenarios.