Towards more accurate object detection via encoding reinforcement and multi-channel enhancement

The existing object detection networks typically apply small kernel convolution that can extract sufficient features for recognizing targets but have poor long-range dependency capability and smaller receptive fields. This paper proposes an object detection network with structure featuring large kernel convolutions and multiple channels. Firstly, the encoding reinforcement module using large kernel convolutions is designed to enlarge the receptive field and improve global feature extraction. Then, the channel enhancement module is constructed to enhance structural information learning. In addition, the encoding reinforcement and channel enhancement are designed in a lightweight way. Finally, the WIOU loss function is introduced to enhance the model’s robustness in poor-quality datasets. In the experiments, the proposed model can achieve optimal performance with similar parameters or computational complexity to existing CNN-based lightweight models.