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Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

Zhengyuan Jiang¹, Siyan Sun¹, Hong Peng¹

¹School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.

International Journal of Neural Systems

|April 14, 2024

Summary

This study introduces the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector, a novel approach for object detection. SNPMiSo enhances feature extraction from single-level maps, achieving improved accuracy and speed compared to existing methods.

Keywords:

Object detection Transformer multi-level feature maps spiking neural P systems

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Area of Science:

Computer Vision
Artificial Intelligence
Deep Learning

Background:

Multi-scale object detection often relies on Feature Pyramid Networks (FPN), increasing complexity.
Single-level feature maps struggle to balance semantic and detail information, limiting performance.
Existing methods face challenges in efficiently extracting multi-scale features for object detection.

Purpose of the Study:

Introduce a novel object detector, Spiking Neural P Multiple-in-Single-out (SNPMiSo), to address limitations in current multi-scale and single-level detectors.
Enhance feature representation by effectively combining semantic and detail information.
Improve both accuracy and speed in object detection tasks.

Main Methods:

Developed the SNPMiSo detector utilizing SNP-like neurons.
Employed two types of Transformers to enhance features across different levels.
Integrated an incremental upsampling module, NAF dilated residual module, and NAF dual-branch detection head for feature merging and detection.

Main Results:

Achieved an Average Precision (AP) of 38.7 on the COCO dataset, a 1.0 AP improvement over YOLOF.
Demonstrated a quicker detection speed compared to advanced multi-level and single-level object detectors.
Successfully extracted multi-scale features and performed object detection tasks effectively.

Conclusions:

The SNPMiSo detector offers a promising solution for efficient and accurate object detection.
The novel architecture effectively balances deep semantic information and shallow detail information.
SNPMiSo presents a competitive alternative to existing object detection methods, offering enhanced performance and speed.