Object detection involves identifying and localizing objects within images or videos, combining classification and localization. Its importance has surged recently, driven by its broad range of practical uses. This research develops a new deep learning model approach for bagging detection and localization in conveyor systems for industrial applications. The proposed method was introduced by investigating multiple branches along with the proposed general derivative pattern for feature fusion. Experimental results show that the proposed method achieved performance comparable to the state-of-the-art object detection systems under normal input conditions. Moreover, the proposed system obtained better results than existing object detection methods under various testing conditions.