This paper introduces a lightweight convolutional neural network, called ECDet, for real-time accurate object detection. In contrast to recent advances of lightweight networks that prefer to use pointwise convolution for changing the number of  feature map's channel, ECDet makes an effort to design equal channel block for constructing the whole backbone network architecture. Meanwhile, we deploy depthwise convolution to compress the feature pyramid network(FPN) detection head further. The experiments show that ECDet only has 3.19 M model size and needs only 3.48B FLOPs with a 416×416 input image. Our method has a 5\% improvement in accuracy compared to YOLO Nano, and it requires less computation. The comprehensive experiments demonstrate that our model achieves promising results in terms of available speed and accuracy trade-off on PASCAL VOC 2007 datasets.