AMSA-CAFF Net: Counting and high-quality density map estimation from X-ray images of electronic components

Accurately estimating object numbers plays a crucial role in industrial applications. However, object counting in computer vision poses a formidable challenge, particularly when dealing with images containing tiny, similar, and stacked objects. Therefore, this paper proposes an encoder-decoder-based convolutional neural network named AMSA-CAFF Net to accurately count and regress high-quality density maps from X-ray images of highly dense microscopic components. AMSA-CAFF Net has three major components: an adaptive multi-scale feature aggregation (AMSA) module for encoding adaptive multi-scale features in the encoder, a channel-wise adaptive feature fusion (CAFF) module to ensure smooth feature fusion, and a continuous feature enhancement mechanism to promote information integration in the decoder. In addition, there is still a lack of datasets dedicated to industrial object counting tasks. To address the limitations associated with the existing datasets, we construct a novel and large-scale object counting dataset named X-ray-based industrial electronic component counting dataset (XRAY-IECCD), consisting of 1,460 X-ray images of 10 types of components, resulting in a total of 2,915,126 objects annotated with points. To the best of our knowledge, the XRAY-IECCD is the first dataset of tiny, dense, multi-class object counts with a substantial number of annotations. We evaluate the performance of AMSA-CAFF Net on three datasets: the XRAY-IECCD, crowd counting benchmark ShanghaiTech dataset, and object counting benchmark CARPK dataset. The proposed framework outperforms the state-of-the-art approaches over the proposed XRAY-IECCD and CARPK datasets, highlighting its effectiveness. Furthermore, we achieve competitive results on the ShanghaiTech dataset, which demonstrates the generalization capability of our method.