###### Abstract  
Thanks to their event-driven asynchronous computing capabilities and low power consumption advantages, spiking neural networks (SNNs) show significant potential for computer vision tasks, especially in object detection. However, effective training methods and optimization mechanisms for SNNs remain underexplored. This study proposes two high accuracy SNNs for object detection, AMS_YOLO and AMSpiking_VGG, integrating neuronal variants and attention mechanisms. To enhance these proposed networks, we explore the impact of incorporating different neuronal variants.The results show that the optimization in the SNN's structure with neuronal variants outperforms that in the attention mechanism for object detection. Compared to the state-of-the-art in the current SNNs, AMS_YOLO improved by 6.7% in accuracy on the static dataset COCO2017, and AMS_Spiking has improved by 11.4% on the dynamic dataset GEN1. object detection; spiking neural networks; neuronal variants; attention mechanism +
Footnote †: journal: Medical  
training algorithms for very large and very deep networks still face the problems of how to encode the impulse signals, gradient vanishing, high training resource overhead and even convergence of the algorithms. In order to solve the end-to-end training problem of deep SNNs, such as threshold-dependent batch normalization (TDBN) [9; 10], SEW block [11], TET [12], MS residual block [13], etc. have emerged. The novel TDBN [10] is trained using spatio-temporal backpropagation [9] for backpropagation, to solve the problem of gradient vanishing in the direct training of deep SNN problems. Therefore, the optimization of SNNs using existing training methods is a problem that needs to be investigated. Compared with traditional ANNs, SNNs exhibits significant advantages in processing spatio-temporal information, achieving self-organizing learning, and adapting to hardware implementation. Therefore, SNNs have attracted much interests in computer vision tasks, such as handwritten digit recognition [14], object detection [15; 16; 17], speaker recognition [18], behavior recognition [19] and optical flow estimation [20; 21]. In particular, researchers have designed SNNs suitable for object detection. [PERSON] et al. proposed Spiking-YOLO [15], the first deep SNN for object detection, addressing inefficiencies in traditional normalization methods and the lack of efficient Leaky-ReLU implementation methods in the SNNs, by introducing channel normalization and sign neurons with imbalanced thresholds. However, this network faces the challenges with high inference time steps and cannot be directly applied to dynamic data from dynamic vision sensor (DVS) [22], a new sensor based on biological vision mechanism. DVS has begun to stand out, in which each pixel independently detects the brightness changes and generates asynchronous event streams, i.e., dynamic data, adapting to SNNs inputs naturally. Consequently, Spiking-VGG [16] is proposed as a dynamic detection framework, which replaces VGG and other backbone networks with SNNs and combines them with single shot multibox detector (SSD) detection heads. EMS-YOLO [17], claiming as a universal model adaptable to both static COCO2017 [23] and dynamic GEN1 [24] datasets, adopts TDBN training approach, first applies directly trained deep SNNs to object detection tasks. However, SNNs still face accuracy limitations, compared with traditional CNNs. Additionally, attention mechanisms such as the convolutional block attention module (CBAM) [25], criss-cross attention [26] and temporal-wise attention [27] mechanisms, have shown significant improvement in feature extraction and model robustness through channel and spatial attention modules. Although some attention mechanisms have been added to SNNs [20; 28; 29], further optimization mechanisms in object detection tasks have stilled need to be explored. This study explores the effects of different neurons on SNNs and integrates attention mechanisms to enhance detection efficiency.