  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.
