  An important component of computer vision research is object detection. In
recent years, there has been tremendous progress in the study of construction
site images. However, there are obvious problems in construction object
detection, including complex backgrounds, varying-sized objects, and poor
imaging quality. In the state-of-the-art approaches, elaborate attention
mechanisms are developed to handle space-time features, but rarely address the
importance of channel-wise feature adjustments. We propose a lightweight
Optimized Positioning (OP) module to improve channel relation based on global
feature affinity association, which can be used to determine the Optimized
weights adaptively for each channel. OP first computes the intermediate
optimized position by comparing each channel with the remaining channels for a
given set of feature maps. A weighted aggregation of all the channels will then
be used to represent each channel. The OP-Net module is a general deep neural
network module that can be plugged into any deep neural network. Algorithms
that utilize deep learning have demonstrated their ability to identify a wide
range of objects from images nearly in real time. Machine intelligence can
potentially benefit the construction industry by automatically analyzing
productivity and monitoring safety using algorithms that are linked to
construction images. The benefits of on-site automatic monitoring are immense
when it comes to hazard prevention. Construction monitoring tasks can also be
automated once construction objects have been correctly recognized. Object
detection task in construction site images is experimented with extensively to
demonstrate its efficacy and effectiveness. A benchmark test using SODA
demonstrated that our OP-Net was capable of achieving new state-of-the-art
performance in accuracy while maintaining a reasonable computational overhead.
