{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:33:55Z","timestamp":1782963235271,"version":"3.54.5"},"reference-count":31,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p> Spiking Neural Networks (SNNs), as a biologically inspired computational model, have garnered significant attention in object detection and image classification due to their event-driven mechanism and low-power characteristics. However, in object detection tasks, the residual structures in conventional networks introduce nonspiking operations, posing a critical challenge for SNNs. To address this issue, we propose a multi-layer feature cascade fusion SNN (MFCF-SNN) for object detection. During feature extraction, our novel multi-level cascaded feature extraction module replaces residual connections with cascade operations, eliminating nonspiking computations while enhancing gradient propagation to deeper layers. For downsampling, we introduce a pooling-convolution module that combines max-pooling and spiking convolution, effectively preserving feature information and improving gradient flow. These two modules collectively ensure pure spike-based computation while facilitating deep network training, thereby enhancing detection accuracy. Experimental results on the PASCAL VOC 2012 and SSDD datasets demonstrate state-of-the-art performance, validating the effectiveness of our approach in advancing SNN-based object detection. <\/jats:p>","DOI":"10.1142\/s0129065725500637","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T02:55:48Z","timestamp":1754708148000},"source":"Crossref","is-referenced-by-count":4,"title":["Multi-layer Feature Cascade Fusion Spiking Neural Network for Object Detection"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6063-5601","authenticated-orcid":false,"given":"Yongqiang","family":"Ma","sequence":"first","affiliation":[{"name":"State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center of Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi 710049, P. R. 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