EnYOLO-World: Improved YOLOv9 and FiLM-Driven PAN for Object Detection

Abstract

Real-time object detection needs to strike a balance between accuracy, speed, and reliability. However,many YOLO-based models struggle with two main issues: weakened feature quality in deeper backbonelayers and inefficient multiscale fusion in the detection neck. To overcome these challenges, we presentEnYOLO-World — a refined detection framework that combines the YOLOv9 GELAN backbone withProgrammable Gradient Information (PGI) to retain detailed spatial features and ensure smoother gradi-ent flow. We also introduce a FiLM-Driven Path Aggregation Network (FiLM-PAN), which adaptivelyfuses multiscale features using channel-wise affine transformations, achieving efficient performance withlinear complexity. Tests on the COCO dataset show that EnYOLO-World offers a better trade-off betweenaccuracy and speed compared to existing real-time detectors, demonstrating the value of optimizing bothbackbone feature retention and neck-level fusion strategies.