Several deep learning algorithms have shown amazing performance for existing object detection tasks, but recognizing darker objects is the largest challenge. Moreover, those techniques struggled to detect or had a slow recognition rate, resulting in significant performance losses. As a result, an improved and accurate detection approach is required to address the above difficulty. The whole study proposes a combination of spiked and normal convolution layers as an energy-efficient and reliable object detector model. The proposed model is split into two sections. The first section is developed as a feature extractor, which utilizes pre-trained VGG16, and the second section of the proposal structure is the combination of spiked and normal Convolutional layers to detect the bounding boxes of images. We drew a pre-trained model for classifying detected objects. With state of the art Python libraries, spike layers can be trained efficiently. The proposed spike convolutional object detector (SCOD) has been evaluated on VOC and Ex-Dark datasets. SCOD reached 66.01% and 41.25% mAP for detecting 20 different objects in the VOC-12 and 12 objects in the Ex-Dark dataset. SCOD uses 14 Giga FLOPS for its forward path calculations. Experimental results indicated superior performance compared to Tiny YOLO, Spike YOLO, YOLO-LITE, Tinier YOLO and Center of loc+Xception based on mAP for the VOC dataset.

翻译：多项深度学习算法在现有目标检测任务中展现了卓越性能，但暗目标识别仍是最大挑战。现有方法存在检测困难或识别速率低下的问题，导致显著性能损失。因此，亟需一种改进的精确检测方法来解决上述难题。本研究提出一种融合脉冲卷积层与常规卷积层的节能可靠目标检测模型。该模型由两部分构成：第一部分采用预训练VGG16作为特征提取器，第二部分通过脉冲卷积层与常规卷积层的组合结构检测图像边界框。我们采用预训练模型对检测目标进行分类。借助先进Python库，脉冲层可被高效训练。所提出的脉冲卷积目标检测器（SCOD）在VOC和Ex-Dark数据集上进行了评估。SCOD在VOC-12数据集的20类目标检测中达到66.01% mAP，在Ex-Dark数据集的12类目标检测中达到41.25% mAP，其前向计算仅需14 Giga FLOPS。实验结果表明，在VOC数据集上，基于mAP指标的评估中，SCOD性能显著优于Tiny YOLO、Spike YOLO、YOLO-LITE、Tinier YOLO及Center of loc+Xception等模型。